Recovering High Dynamic Range (HDR) images from multiple Standard Dynamic Range (SDR) images becomes challenging when the SDR images exhibit noticeable degradation and missing content. Leveraging scene-specific semantic priors offers a promising solution for restoring heavily degraded regions. However, these priors are typically extracted from sRGB SDR images, the domain/format gap poses a significant challenge when applying it to HDR imaging. To address this issue, we propose a general framework that transfers semantic knowledge derived from SDR domain via self-distillation to boost existing HDR reconstruction. Specifically, the proposed framework first introduces the Semantic Priors Guided Reconstruction Model (SPGRM), which leverages SDR image semantic knowledge to address ill-posed problems in the initial HDR reconstruction results. Subsequently, we leverage a self-distillation mechanism that constrains the color and content information with semantic knowledge, aligning the external outputs between the baseline and SPGRM. Furthermore, to transfer the semantic knowledge of the internal features, we utilize a Semantic Knowledge Alignment Module (SKAM) to fill the missing semantic contents with the complementary masks. Extensive experiments demonstrate that our framework significantly boosts HDR imaging quality for existing methods without altering the network architecture.

We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Motion points for initialization. While learning-based Multi-view Stereo (MVS) provides dense 3D points, directly combining it with Gaussian Splatting leads to suboptimal results due to the ill-posed nature of sparse-view geometric optimization. We propose Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction. Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under illposed conditions. Sparse2DGS outperforms existing methods by notable margins while being 2× faster than the NeRF-based fine-tuning approach. Code is available at https://github.com/Wuuu3511/Sparse2DGS.

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity. The former enforces small distances for red coordinates to remove the red artifacts, while the latter compresses the low-light regions to remove the black artifacts. To fully leverage the chromatic and intensity information, a novel Color and Intensity Decoupling Network (CIDNet) is further introduced to learn accurate photometric mapping function under different lighting conditions in the HVI space. Comprehensive results from benchmark and ablation experiments show that the proposed HVI color space with CIDNet outperforms the state-of-the-art methods on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.

Pairwise camera pose estimation from sparsely overlapping image pairs remains a critical and unsolved challenge in 3D vision. Most existing methods struggle with image pairs that have small or no overlap. Recent approaches attempt to address this by synthesizing intermediate frames using video interpolation and selecting key frames via a self-consistency score. However, the generated frames are often blurry due to small overlap inputs, and the selection strategies are slow and not explicitly aligned with pose estimation. To solve these cases, we propose Hybrid Video Generation (HVG) to synthesize clearer intermediate frames by coupling a video interpolation model with a pose-conditioned novel view synthesis model, where we also propose a Feature Matching Selector (FMS) based on feature correspondence to select intermediate frames appropriate for pose estimation from the synthesized results. Extensive experiments on Cambridge Landmarks, ScanNet, DL3DV-10K, and NAVI demonstrate that, compared to existing SOTA methods, PoseCrafter can obviously enhance the pose estimation performances, especially on examples with small or no overlap.

Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same instance. While such a feature learning scheme achieves promising performance, learning discriminative features solely based on visual information is challenging especially in case of environmental interference such as occlusion, blur and domain variance. In this work, we argue that multi-modal language-driven features provide complementary information to classical visual features, thereby aiding in improving the robustness to such environmental interference. To this end, we propose a new multi-object tracking framework, named LG-MOT, that explicitly leverages language information at different levels of granularity (scene-and instance-level) and combines it with standard visual features to obtain discriminative representations. To develop LG-MOT, we annotate existing MOT datasets with scene-and instance-level language descriptions. We then encode both scene-and instance-level language information into high-dimensional embeddings, which are utilized to guide the visual features during training. At inference, our LG-MOT uses the standard visual features without relying on annotated language descriptions. Extensive experiments on three benchmarks, MOT17, DanceTrack and SportsMOT, reveal the merits of the proposed contributions leading to state-of-the-art performance. On the DanceTrack test set, our LG-MOT achieves an absolute gain of 2.2% in terms of target object association (IDF1 score), compared to the baseline using only visual features. Further, our LG-MOT exhibits strong cross-domain generalizability. Source code and pre-trained models are available at https://github.com/WesLee88524/LG-MOT.

Actual image super-resolution is an extremely challenging task due to complex degradations existing in the image. To solve this problem, two dominant methodologies have emerged: degradation-estimation-based Addressing actual image super-resolution remains a formidable challenge due to the intricate degradations present in images. Two primary methodologies have emerged: degradation-estimation-based and blind-based methods. The former often struggle to accurately estimate degradation, limiting their effectiveness on real low-resolution images. Conversely, blind-based methods rely on a single perceptual perspective, constraining their adaptability to diverse perceptual characteristics. In response to these challenges, we present MPF-Net, a novel super-resolution approach aimed at enhancing real-world image super-resolution tasks by enabling the model to learn multiple perceptual features from input images. Our method features a Multi-Perception Feature Extraction module (MPFE) designed to extract diverse perceptual details, complemented by Cross-Perception Blocks (CPB) facilitating the fusion of this information for efficient super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) to enhance the model’s learning by leveraging newly generated HR and LR images as positive and negative samples. Experimental results on challenging real-world SR datasets demonstrate the superiority of our approach over existing state-of-the-art methods, both qualitatively and quantitatively.

Single image super-resolution (SISR), especially in the real world, usually builds a large amount of LR-HR image pairs to learn representations that contain rich textural and structural information. However, relying on massive data for model training not only reduces training efficiency, but also causes heavy data storage burdens. In this paper, we attempt a pioneering study on dataset distillation (DD) for SISR problems to explore how data could be slimmed and compressed for the task. Unlike previous coreset selection methods which select a few typical examples directly from the original data, we remove the limitation that the selected data cannot be further edited, and propose to synthesize and optimize samples to preserve more task-useful representations. Concretely, by utilizing pre-trained GANs as a suitable approximation of realistic data distribution, we propose GSDD, which distills data in a latent generative space based on GAN-inversion techniques. By optimizing them to match with the practical data distribution in an informative feature space, the distilled data could then be synthesized. Experimental results demonstrate that when trained with our distilled data, GSDD can achieve comparable performance to the state-of-the-art (SOTA) SISR algorithms, while a nearly ×8 increase in training efficiency and a saving of almost 93.2% data storage space can be realized. Further experiments on challenging real-world data also demonstrate the promising generalization ability of GSDD.

Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, di-rectly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related meth-ods either seek selective aggregation or improve aggregated depth in the 2D space, both are unable to handle geomet-ric inconsistency in the cost volume effectively. In this pa-per, we propose GoMVS to aggregate geometrically consis-tent costs, yielding better utilization of adjacent geometries. More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geomet-ric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals, then uses the correspondence to propa-gate adjacent costs to the reference geometry, followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU, Tanks & Temple, and ETH3D datasets. Notably, our method ranks 1st on the Tanks & Temple Advanced benchmark. Code is available at https://github.com/Wuuu3511IGoMVS.

The Joint Detection and Embedding (JDE) framework has achieved remarkable progress for multiple object tracking. Existing methods often employ extracted embeddings to re-establish associations between new detections and previously disrupted tracks. However, the reliability of embeddings diminishes when the region of the occluded object frequently contains adjacent objects or clutters, especially in scenarios with severe occlusion. To alleviate this problem, we propose a novel multiple object tracking method based on visual embedding consistency, mainly including: 1) Occlusion Prediction Module (OPM) and 2) Occlusion-Aware Association Module (OAAM). The OPM predicts occlusion information for each true detection, facilitating the selection of valid samples for consistency learning of the track’s visual embedding. The OAAM leverages occlusion cues and visual embeddings to generate two separate embeddings for each track, guaranteeing consistency in both unoccluded and occluded detections. By integrating these two modules, our method is capable of addressing track interruptions caused by occlusion in online tracking scenarios. Extensive experimental results demonstrate that our approach achieves promising performance levels in both unoccluded and occluded tracking scenarios.

Traditional frame-based cameras inevitably suffer from non-uniform blur in real-world scenarios. Event cameras that record the intensity changes with high temporal resolution provide an effective solution for image deblurring. In this paper, we formulate the event-based image deblurring as an image generation problem by designing diffusion priors for the image and residual. Specifically, we propose an alternative diffusion sampling framework to jointly estimate clear and residual images to ensure the quality of the final result. In addition, to further enhance the subtle details, a pseudoinverse guidance module is leveraged to guide the prediction closer to the input with event data. Note that the proposed method can effectively handle the real unknown degradation without kernel estimation. The experiments on the benchmark event datasets demonstrate the effectiveness of our method.

Infrared small target detection (IRSTD) is crucial in both military and civilian applications. However, challenges such as low contrast, low signal-to-noise ratio (SNR), and lack of shape and texture information limit the effectiveness of existing methods in capturing edge details and representing target areas. To address these issues, we propose the dual-stream edge-target learning network (DETL-Net) for IRSTD. This network enhances feature cross-fusion by learning edge details and target regions through a dual-stream framework, significantly improving detection performance. Specifically, we extract multilevel features of the image based on the encoder-decoder structure of U-Net and then reconstruct the feature map. In the decoder, we propose the dual-guided cross-fusion module (DGCFM) to capture edge details of small targets and global contextual features of the target region, achieving complementary advantages. The multiscale context fusion module (MCFM) within DGCFM uses central difference convolution to enhance local contrast and extract rich contextual details, thereby retaining edge information and enhancing overall target representation. In addition, we introduce the cross-dimension interactive aggregation attention module (CIAAM), which dynamically adjusts feature fusion weights across layers to effectively suppress noise and enhance the discrimination of small targets. These modules are sequentially interconnected to progressively refine edge details, and the acquired target features are subsequently utilized for predicting the final target mask via the segmentation head. Experiments on the NUAA-SIRST and IRSTD-1k datasets demonstrate that DETL-Net outperforms state-of-the-art (SOTA) methods. The source code is available athttps://github.com/rayyao/DETL-Net.

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing tasks such as face superresolution, face enhancement, and face editing. To narrow this gap, in this article, we first introduce the largest annotated IQA database developed to date, which contains 20,000 human faces – an order of magnitude larger than all existing rated datasets of faces – of diverse individuals in highly varied circumstances. Based on the database, we further propose a novel deep learning model to accurately predict face image quality, which, for the first time, explores the use of generative priors for IQA. By taking advantage of rich statistics encoded in well pretrained off-the-shelf generative models, we obtain generative prior information and use it as latent references to facilitate blind IQA. The experimental results demonstrate both the value of the proposed dataset for face IQA and the superior performance of the proposed model.

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method.

Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.

Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNNs to avoid overfitting when trained on only a few images. In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Unlikely previous methods, directly recovering content and removing ghosts simultaneously, which is hard to achieve optimum, we first generate content of saturated regions with a self-supervised mechanism and then address ghosts via an iterative semi-supervised learning framework. Concretely, considering that saturated regions can be regarded as masking Low Dynamic Range (LDR) input regions, we design a Saturated Mask AutoEncoder (SMAE) to learn a robust feature representation and reconstruct a non-saturated HDR image. We also propose an adaptive pseudo-label selection strategy to pick high-quality HDR pseudo-labels in the second stage to avoid the effect of mislabeled samples. Experiments demonstrate that SSHDR outperforms state-of-the-art methods quantitatively and qualitatively within and across different datasets, achieving appealing HDR visualization with few labeled samples.

Deep learning (DL) based image super-resolution (SR) tech-niques have been well investigated for recent years. However, studies dedicated to SR image quality assessment (SR-IQA) have not been fully developed, which is even more difficult if pristine high-resolution (HR) images are lacking as a reference. Due to the challenge, existing widely used no-reference (NR) SR-IQA metrics (e.g., PI, NIQE, and Ma) are still far from meeting the practical requirements of providing accurate estimations which align well with human mean opinion scores (MOS). To this end, we propose a novel Knowledge Extension Super-Resolution Image Quality Assessment (KE-SR-IQA) framework to predict SR image quality by leveraging a semi-supervised knowledge distillation (KD) strategy. Concretely, we first employ a well-trained full-reference (FR) SR-IQA model as the teacher, then we perform knowledge extension (KE) by additional pseudo-labeled data to further distill a NR-student for promoting the prediction accuracy. Extensive experiments on several benchmarks validate the ef-fectiveness of our approach.

Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various-level noises can help obtain a satisfied high-resolution image for the low-resolution one. To further improve the performance and simplify current DPM-based super-resolution methods, we propose a simple but non-trivial DPM-based super-resolution post-process framework, i.e., cDPMSR. After applying a pre-trained SR model on the to-be-test LR image to provide the conditional input, we adapt the standard DPM to conduct conditional image generation and perform super-resolution through a deterministic iterative denoising process. Our method surpasses prior attempts on both qualitative and quantitative results and can generate more photo-realistic counterparts for the low-resolution images with various benchmark datasets including Set5, Set14, Urban100, BSD100, and Manga109. Code will be published after accepted.

The aim of image restoration is to recover high-quality images from distorted ones. However, current methods usually focus on a single task (e.g., denoising, deblurring or super-resolution) which cannot address the needs of real-world multi-task processing, especially on mobile devices. Thus, developing an all-in-one method that can restore images from various unknown distortions is a significant challenge. Previous works have employed contrastive learning to learn the degradation representation from observed images, but this often leads to representation drift caused by deficient positive and negative pairs. To address this issue, we propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration. AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering, without any prior knowledge of degradation information. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration. To further enhance the performance of the image restoration network and overcome domain gaps caused by unknown distortions, we design a feature transform block (FTB) that aligns domains and refines features with the guidance of the degradation representation. We conduct extensive experiments on multiple distorted datasets, demonstrating the effectiveness of our method and its advantages over state-of-the-art restoration methods both qualitatively and quantitatively.

Abstract Anomaly detection aims at locating the spectral different objects of a specific scene without any prior information, and has gained increasing attention. By decomposing the input hyperspectral image (HSI) into a background tensor and an anomaly tensor, the tensor approximation is an efficient tool for detecting the anomalies. Low rankness is usually utilized as the regularizer during the background reconstruction process. Different from most existing hyperspectral anomaly detection methods which compute the truncated nuclear norm of the third folding of the original HSI, a novel weighted‐sparsity‐regularized tensor linear representation (WsrTLR) method is proposed for hyperspectral anomaly detection in this paper. Tensor linear representation is utilized to formulate the background HSI by a three‐dimensional (3D) representation base and the corresponding 3D representation coefficient. Low rankness is applied to constrict the representation coefficient, an operation which avoids destroying the multi‐way structure and losing information during the matrixing process, and ensures a satisfactory detection accuracy. Meanwhile, by incorporating the weighted‐sparsity‐regularized tensor linear representation to reconstruct the background tensor, the anomalies can be easily detected by eliminating the background tensor from the original scene. In addition, to avoid negative influence caused by the redundant bands and noisy bands in the representation process, informative bands have been first selected via an optimal neighborhood reconstruction strategy. Experimental results and data analysis on four real hyperspectral datasets, which contain anomalies with different sizes, have demonstrated the effectiveness of the proposed method.

With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.

Self-supervised monocular depth estimation has succeeded in learning scene geometry from only image pairs or sequences. However, it is still highly ill-posed for self-supervised depth estimation to generate high-quality depth maps with both global high accuracy and local fine details. To address this issue, we propose a novel frequency-based recurrent refinement scheme to improve the self-supervised depth estimation. Since the global and local depth representation can be correlated to high/low frequency coefficients in the frequency domain, we propose a frequency-based recurrent depth coefficient refinement (RDCR) scheme, which progressively refines both low frequency and high frequency depth coefficients with an RNN-based architecture in a multi-level manner. During the recurrent process, the depth coefficients generated from the previous time step are used as the input to generate the current depth coefficients, yielding progressively optimized depth estimations. Meanwhile, considering that the depth details often appear in areas with high image frequency, we further improve depth details during the RDCR process by leveraging the image-based high frequency components. Specifically, in each RDCR module, we enhance the high frequency depth representations by selecting and feeding the informative image-based high frequency features with a learned feature weighting mask. Extensive experiments show that the proposed method achieves globally accurate estimation with fine local details, outperforming other self-supervised methods in both quantitative and qualitative comparisons.

In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obtaining better restored results, fewer have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP). Specifically, We first establish a dynamic scene imaging dataset containing composite distortions and applied image restoration processes to validate the rationality of the existence to IRP. Based on this dataset, we investigate several properties of IRP and propose a novel deep model to accurately predict IRP values. By gradually distilling and selective fusing the degradation features, the proposed model shows its superiority in IRP prediction. Thanks to the proposed model, we are then able to validate how various image restoration related applications are benefited from IRP prediction. We show the potential usages of IRP as a filtering principle to select valuable frames, an auxiliary guidance to improve restoration models, and also an indicator to optimize camera settings for capturing better images under dynamic scenarios.

Deep learning (DL) based single image super-resolution (SISR) algorithms have now achieved highly satisfactory evaluation and visualization results on synthetic datasets. However, in some practical applications, especially when restoring some real-world low-resolution (LR) photos, the limitation and unicity of the most commonly used bicubic down-sampling kernel often lead to significant performance degradation of models trained under ideal conditions. Thus, we first propose a kernel augmentation (KA) strategy based on generative adversarial networks (GANs) to improve the generalization ability and robustness of current SISR models. Then, we intend to reconstruct the stochastic variation (SV) features that are widely present in natural images to obtain a more realistic feature representation. In the end, extensive experiments demonstrate the feasibility and effectiveness of our approach in dealing with real-world SISR problems.

Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework.

At present, most mainstream algorithms for single image super-resolution (SISR) assume the image degradation process as an ideal degradation process (e.g. bicubic downscaling), which violates the actual degeneration conditions. In real-world image capturing, objects often move in a dynamic environment, and camera shake also often occurs, which results in serious blurs. Our work focuses on the task of image super-resolution with heavy motion blur, for which we adopt a network with two branches: one branch for image deblurring and the other one for super-resolution. Since the features obtained by the deblurring are rich in details, we apply their features as supplementary information to the super-resolution branch. Based on the adopted dual-branch framework, our major technical novelties lie in two novel modules: Multi-Scale Feature Fusion (MSFF1) module which fuses features of different scale from the deblurring branch to get local and global information, and Multi-Stage Feature Fusion (MSFF2) module which further filters useful information with attention. We evaluate the proposed method under various blur scenarios on the benchmark datasets, demonstrating competitive performance against existing methods.

Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to real-world distorted images. To deal with the challenge, we propose a self-adaptive hyper network architecture to blind assess image quality in the wild. We separate the IQA procedure into three stages including content understanding, perception rule learning and quality predicting. After extracting image semantics, perception rule is established adaptively by a hyper network, and then adopted by a quality prediction network. In our model, image quality can be estimated in a self-adaptive manner, thus generalizes well on diverse images captured in the wild. Experimental results verify that our approach not only outperforms the state-of-the-art methods on challenging authentic image databases but also achieves competing performances on synthetic image databases, though it is not explicitly designed for the synthetic task.

The captured images under low-light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vision. A variety of methods have been proposed for this task, but these methods often failed in an extreme low-light environment and amplified the underlying noise in the input image. To address such a difficult problem, this paper presents a novel attention-based neural network to generate high-quality enhanced low-light images from the raw sensor data. Specifically, we first employ attention strategy (i.e. spatial attention and channel attention modules) to suppress undesired chromatic aberration and noise. The spatial attention module focuses on denoising by taking advantage of the non-local correlation in the image. The channel attention module guides the network to refine redundant colour features. Furthermore, we propose a new pooling layer, called inverted shuffle layer, which adaptively selects useful information from previous features. Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.

Self-supervised depth estimation has shown great prospects in inferring 3D structures using purely unannotated images. However, its performance usually drops when trained on the images with changing brightness and moving objects. In this paper, we address this issue by enhancing the robustness of the self-supervised paradigm using a set of image-based and geometry-based constraints. Our contributions are threefold, 1) we propose a gradient-based robust photometric loss which restrains the false supervisory signals caused by brightness changes, 2) we propose to filter out the unreliable areas that violate the rigid assumption by a novel combined selective mask, which is computed on the forward pass of the network by leveraging the inter-loss consistency and the loss-gradient consistency, and 3) we constrain the motion estimation network to generate across-frame consistent motions via proposing a triplet-based cycle consistency constraint. Extensive experiments conducted on KITTI, Cityscape and Make3D datasets demonstrate the superiority of our method, that the proposed method can effectively handle complex scenes with changing brightness and object motions. Both qualitative and quantitative results show that the proposed method outperforms the state-of-the-art methods.

One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through aligning LDR inputs based on optical flow or detecting anomalies among them. However, they often fail to produce satisfactory results in practice, since the real object motion can be very large. In this study, we present a novel deep framework, termed NHDRRnet, which adopts an alternative direction and attempts to remove ghosting artifacts by exploiting the non-local correlation in inputs. In NHDRRnet, we first adopt an Unet architecture to fuse all inputs and map the fusion results into a low-dimensional deep feature space. Then, we feed the resultant features into a novel global non-local module which reconstructs each pixel by weighted averaging all the other pixels using the weights determined by their correspondences. By doing this, the proposed NHDRRnet is able to adaptively select the useful information (e.g., which are not corrupted by large motions or adverse lighting conditions) in the whole deep feature space to accurately reconstruct each pixel. In addition, we also incorporate a triple-pass residual module to capture more powerful local features, which proves to be effective in further boosting the performance. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed NDHRnet in terms of suppressing the ghosting artifacts in HDR reconstruction, especially when the objects have large motions.

In this paper, we propose a hybrid Structure-from-Motion scheme which combines the strength of both global and local incremental SfM methods to get a drift-free and accurate estimation with lower time consumption. More specifically, we propose to construct a robust maximum leaf spanning tree (RMLST) from the initial scene graph and further expand it to a robust graph (RG) to grasp the global picture of camera distribution and scene structure. Then the views in the robust graph are solved in global manner as an initial estimation. After that, the remaining views are estimated with the proposed community-based local incremental approach to guarantee local accuracy and scalability. Bundle adjustment is conducted to optimize the estimation. Experiments show that our method is robust and free from the scene drift as global SfM, and shows much better efficiency than incremental approaches. Besides, our algorithm achieves higher accuracy compared with the state-of-the-art methods.

Generating a high dynamic range (HDR) image from a set of sequential exposures is a challenging task for dynamic scenes. The most common approaches are aligning the input images to a reference image before merging them into an HDR image, but artifacts often appear in cases of large scene motion. The state-of-the-art method using deep learning can solve this problem effectively. In this paper, we propose a novel deep convolutional neural network to generate HDR, which attempts to produce more vivid images. The key idea of our method is using the coarse-to-fine scheme to gradually reconstruct the HDR image with the multi-scale architecture and residual network. By learning the relative changes of inputs and ground truth, our method can produce not only artificial free image but also restore missing information. Furthermore, we compare to existing methods for HDR reconstruction, and show high-quality results from a set of low dynamic range (LDR) images. We evaluate the results in qualitative and quantitative experiments, our method consistently produces excellent results than existing state-of-the-art approaches in challenging scenes.

Motion blurred images are widely existing when using a hand-held camera especially under the dim lighting conditions. Since edge information contained in the noisy image may be blurred by the motion blur, a blurred/noisy image pair captured under different exposure time can help to restore a sharp image. In the traditional deblurring methods based on blurred/noisy image pair, the deblurring process is in series with the denoising process, so that restoration result is sensitive to the denoised result. In this paper, we propose a robust algorithm to obtain the sharp image by fusing the blurred image and noisy image. By joint modeling the deblurring model and denoising model, the restoration result can be optimized via estimating the sharp image and blur kernel alternately in the proposed methods, and it is not sensitive to the denoised result benefited by the joint model. Experimental results demonstrated that the proposed method can achieve better performance compared with the state-of-the-art single image denoising methods, single image deblurring methods and blurred/noisy pair deblurring methods.

In this paper, we propose a novel neighbor combination framework for the reconstruction of the atmospheric turbulence degenerated image sequence. To utilize the spatial and temporal redundancy, a neighbor vector sampling strategy in spatial and temporal domain is conducted relying on the modeling of the registered sequence. Then, a combinator of neighbor vectors is developed based on a resampling maximum likelihood model and a relative approximation. Relying on the neighbor combination and spatial-invariant deconvolution, a clear image is reconstructed. Experiments on real data sets demonstrate the effectiveness of this framework.

The influence of noises is obviously in the image transform method based on the pixels elements representation of images. To some extent the method of image transform based on linear elements representation can solution the problem. The Beamlet transform is an effective method of line segment extraction. This work improved the traditional Beamlet transform by considering the directional information of lines. The improved method transformed a digital image to a coefficient matrix. This method can embody the linear singularity of some linear targets and can be used for edge detection and extracting other useful targets in noisy images. The experimental results on manual images and SAR images demonstrate the effectiveness of this method.

In this paper, a starry image matching method based on isomorphism sub graph and LCS (Longest Common Sub-sequence) is proposed for dim and small space target detecting. A starry image mainly consists of a background with a large number of low-gray pixels and bright but small facular. The relative location of the stars can be regarded as a feature set for matching in the form of a graph. While matching two graphs, the LCS is applied for measuring the similarity and acquiring the isomorphism sub graph. Due to the characteristic of the starry image, this method could deal with the images with more rotation and shift. Experimental results show that this method can provide a good way for the trajectory acquisition of dim and small targets.

In this article, the selective visual attention mechanism and curve detection by Connect The Dots model is introduced to small and dim target detection in deep space background. The greyscale and movement continual significance are fully taken into account to get focus of attention integration map. A curve detection method which based on Connect The Dots model is designed to detect the target trajectory. Qualitative and quantitative results prove that the proposed algorithm has strong anti-noise performance and improve calculate efficiency of detection system effectively.