Single image super-solution (SR) aims to restore a high-resolution (HR) image from a degraded low-resolution (LR) image. However, existing SR models still face a significant domain gap between synthetic and real-world datasets due to the mismatched degradation distributions, hindering SR models from achieving optimal results. In this paper, we propose an unsupervised diffusion-based degradation modeling framework (UDDM) to effectively capture real-world degradation distributions. Specifically, given unpaired LR and HR images, a diffusion-based degradation module (DDM) first models the degradation distribution by diffusing real-world LR images to downsampled LR images, which does not require HR images. It then applies reverse diffusion to generate real-world LR images from extremely downsampled HR images. This approach allows DDM to model and generate real-world degradation distributions without requiring paired data, by using extreme downsampling to link unpaired LR and HR images. Additionally, we introduce a physics-based dynamic degradation module (P-DDM) that adaptively models content-aware degradation, ensuring both content and structural accuracy. Finally, the LR images generated by DDM and P-DDM are adaptively weighted to produce the final LR images, which are paired with the given HR images for training the SR network. Extensive experiments across multiple real-world datasets demonstrate that our framework achieves state-of-the-art performance in both qualitative and quantitative comparison.

Reflection removal is challenging due to complex light interactions, where reflections obscure important details and hinder scene understanding. Polarization naturally provides a powerful cue to distinguish between reflected and transmitted light, enabling more accurate reflection removal. However, existing methods often rely on small-scale or synthetic datasets, which fail to capture the diversity and complexity of real-world scenarios. To this end, we construct a large-scale dataset, PolaRGB, for Polarization-based reflection removal of RGB images, which enables us to train models that generalize effectively across a wide range of real-world scenarios. The PolaRGB dataset contains 6,500 well-aligned mixed-transmission image pairs, 8× larger than existing polarization datasets, and is the first to include both RGB and polarization images captured across diverse indoor and outdoor environments with varying lighting conditions. Besides, to fully exploit the potential of polarization cues for reflection removal, we introduce PolarFree, which leverages diffusion process to generate reflection-free cues for accurate reflection removal. Extensive experiments show that PolarFree significantly enhances image clarity in challenging reflective scenarios, setting a new benchmark for polarized imaging and reflection removal. Code and dataset are available at https://github.com/mdyao/PolarFree.

Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, high-fidelity view synthesis, but remain critically dependent on camera poses estimated by Structure-from-Motion (SfM), which is notoriously unreliable in textureless indoor environments. To eliminate this dependency, recent pose-free variants have been proposed, yet they often fail under abrupt camera motion due to unstable initialization and purely photometric objectives. In this work, we introduce **Nope-RoomGS**, an optimization framework with no need for camera pose inputs, which effectively addresses the textureless regions and abrupt camera motion in indoor room environments through a local-to-global optimization paradigm for 3DGS reconstruction. In the local stage, we propose a lightweight local neural geometric representation to bootstrap a set of reliable local 3D Gaussians for separated short video clips, regularized by multi-frame tracking constraints and foundation model depth priors. This enables reliable initialization even in textureless regions or under abrupt camera motions. In the global stage, we fuse local 3D Gaussians into a unified 3DGS representation through an alternating optimization strategy that jointly refines camera poses and Gaussian parameters, effectively mitigating gradient interference between them. Furthermore, we decompose camera pose optimization based on a piecewise planarity assumption, further enhancing robustness under abrupt camera motion. Extensive experiments on Replica, ScanNet and Tanks & Temples demonstrate the state-of-the-art performance of our method in both camera pose estimation and novel view synthesis.

Modern end-to-end image signal processors (ISPs) can learn complex mappings from RAW/XYZ data to sRGB (and vice versa), opening new possibilities in image processing. However, the growing diversity of camera models, particularly in mobile devices, renders the development of individual ISPs unsustainable due to their limited versatility and adaptability across varied camera systems. In this paper, we introduce Uni-ISP, a novel pipeline that unifies ISP learning for diverse mobile cameras, delivering a highly accurate and adaptable processor. The core of Uni-ISP is leveraging device-aware embeddings through learning forward/inverse ISPs and its special training scheme. By doing so, Uni-ISP not only improves the performance of forward and inverse ISPs but also unlocks new applications previously inaccessible to conventional learned ISPs. To support this work, we construct a real-world 4K dataset, FiveCam, comprising more than 2,400 pairs of sRGB-RAW images captured synchronously by five smartphone cameras. Extensive experiments validate Uni-ISP's accuracy in learning forward and inverse ISPs (with improvements of +2.4dB/1.5dB PSNR), versatility in enabling new applications, and adaptability to new camera models.

Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due to the heterogeneous nature of different types of degradations, it is difficult to process multiple degradations in a single network. To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations. The learned NDR adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components. Subsequently, we develop a degradation query module and a degradation injection module to effectively approximate and utilize the specific degradation based on NDR, enabling the all-in-one restoration ability for multiple degradations. Moreover, we propose a bidirectional optimization strategy to effectively drive NDR to learn the degradation representation by optimizing the degradation and restoration processes alternately. Comprehensive experiments on representative types of degradations (including noise, haze, rain, and downsampling) demonstrate the effectiveness and generalizability of our method. Code is available at https://github.com/mdyao/NDR-Restore.

The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of Ze-DuSR over existing solutions on both synthesized and real-world dual-lens datasets. The implementation code is available at https://github.com/XrKang/ZeDuSR.

Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsicingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamentalingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via aningredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.

Lightness adaptation is vital to the success of image processing to avoid unexpected visual deterioration, which covers multiple aspects, e.g., low-light image enhancement, image retouching, and inverse tone mapping. Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability. To address this limitation, we propose a novel generalized lightness adaptation algorithm that extends conventional normalization techniques through a channel filtering design, dubbed Channel Selective Normalization (CSNorm). The proposed CSNorm purposely normalizes the statistics of lightness-relevant channels and keeps other channels unchanged, so as to improve feature generalization and discrimination. To optimize CSNorm, we propose an alternating training strategy that effectively identifies lightness-relevant channels. The model equipped with our CSNorm only needs to be trained on one lightness condition and can be well generalized to unknown lightness conditions. Experimental results on multiple benchmark datasets demonstrate the effectiveness of CSNorm in enhancing the generalization ability for the existing lightness adaptation methods. Code is available at https://github.com/mdyao/CSNorm.

Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information while filtering out irrelevant information for the fused result. However, existing methods address this problem by leveraging static convolutional neural networks (CNNs), suffering two inherent limitations during feature extraction,i.e., being unable to handle spatial-variant contents and lacking guidance from multiple inputs. In this paper, we propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Specifically, we design a mutual-guided dynamic filter (MGDF) for adaptive feature extraction, composed of a mutual-guided cross-attention (MGCA) module and a dynamic filter predictor, where the former incorporates additional guidance from different inputs and the latter generates spatial-variant kernels for different locations. In addition, we introduce a parallel feature fusion (PFF) module to effectively fuse local and global information of the extracted features. To further reduce the redundancy among the extracted features while simultaneously preserving their shared structural information, we devise a novel loss function that combines the minimization of normalized mutual information (NMI) with an estimated gradient mask. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks. The code and model are publicly available at: https://github.com/Guanys-dar/MGDN.

Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks. Although image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks. Specifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint. TCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs. We provide various formats to utilize TCN for image enhancement, including seamless integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures. Through extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements. Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation. The code is available at \textit{\textcolor{blue}{https://github.com/huangkevinj/TCNorm}}.

Self-supervised denoising frameworks have recently been proposed to learn denoising models without noisy-clean image pairs, showing great potential in various applications. The denoising model is expected to produce visually pleasant images without noise patterns. However, it is non-trivial to achieve this goal using self-supervised methods because 1) the self-supervised model is difficult to restore the perceptual information due to the lack of clean supervision, and 2) perceptual quality is relatively subjective to users’ preferences. In this paper, we make the first attempt to build an interactive self-supervised denoising model to tackle the aforementioned problems. Specifically, we propose an interactive two-branch network to effectively restore perceptual information. The network consists of a denoising branch and an interactive branch, where the former focuses on efficient denoising, and the latter modulates the denoising branch. Based on the delicate architecture design, our network can produce various denoising outputs, allowing the user to easily select the most appealing outcome for satisfying the perceptual requirement. Moreover, to optimize the network with only noisy images, we propose a novel two-stage training strategy in a self-supervised way. Once the network is optimized, it can be interactively changed between noise reduction and texture restoration, providing more denoising choices for users. Existing self-supervised denoising methods can be integrated into our method to be user-friendly with interaction. Extensive experiments and comprehensive analyses are conducted to validate the effectiveness of the proposed method.

With the popularization of ultra high definition (UHD) high dynamic range (HDR) displays, recent works focus onupgradinghigh definition (HD) standard dynamic range (SDR) videos to UHD-HDR versions, aiming to provides richer details and higher contrasts on advanced modern displays. However, joint considering theupgrading&downgradingtranslations between two types of videos, which is practical in real applications, is generally neglected. On the one hand,downgradingtranslation is the key to showing UHD-HDR videos on HD-SDR displays. On the other hand, considering both translations enables joint optimization and results in high quality translation. To this end, we propose the bidirectional translation network (BiT-Net), which jointly considers two translations in one network for the first time. In brief, BiT-Net is elaborately designed in aninvertiblefashion that can be efficiently inferred along forward and backward directions fordowngradingandupgradingtasks, respectively. Based on this framework, we divide each direction into three sub-tasks,i.e., decomposition, structure-guided translation, and synthesis, to effectively translate the dynamic range and the high-frequency details. Benefiting from the dedicated architecture, our BiT-Net can work on 1) downgrading UHD-HDR videos, 2) upgrading existing HD-SDR videos, and 3) synthesizing UHD-HDR versions from the downgraded HD-SDR videos. Experiments show that the proposed method achieves state-of-the-art performances on all these three tasks.

Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel. To improve real world applicability of such models, there are growing interests to develop models optimized for arbitrary upscaling factors. Our proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process. Using joint optimization of both directions, the proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling. It improves the performance of current arbitrary upscaling models by a large margin while at the same time learns to maintain visual perception quality in downscaled images. The proposed model is further shown to be robust in cycle idempotence test, free of severe degradations in reconstruction accuracy when the downscaling-to-upscaling cycle is applied repetitively. This robustness is beneficial for image rescaling in the wild when this cycle could be applied to one image for multiple times. It also performs well on tests with arbitrary large scales and asymmetric scales, even when the model is not trained with such tasks. Extensive experiments are conducted to demonstrate the superior performance of our model.

Images captured under improper exposures including underexposure and overexposure often suffer from unsatisfactory visual effects. Since their correction procedures are quite different, it is challenging for a single network to correct various exposures. The key to addressing this issue is consistently learning underexposure and overexposure corrections. To achieve this goal, we propose an Exposure-Consistency Processing (ECP) module to consistently learn the representation of both underexposure and overexposure in the feature space. Specifically, the ECP module employs the bilateral activation mechanism that derives both underexposure and overexposure property features for exposure-consistency representation modeling, which is followed by two shared-weight branches to process these features. Based on the ECP module, we build the whole network by utilizing it as the basic unit. Additionally, to further assist the exposure-consistency learning, we develop an Exposure-Consistency Constraining (ECC) strategy that augments the various local region exposures and then constrains the feature representation change between the exposure augmented image and the original one. Our proposed network is lightweight and outperforms existing methods remarkably, while the ECP module can also be extended to other baselines, demonstrating its superiority and scalability. code: https://github.com/KevinJ-Huang/ECLNet.

Stereo image pairs record the scene from two different views and introduce cross-view information for image restoration. However, there are two challenges in utilizing the cross-view information for stereo image restoration: cross-view alignment and information fusion. Most existing methods adopt convolutional neural networks to align the views and fuse the information locally, which has difficulty in capturing the global correspondence across stereo images for view alignment and makes it hard to integrate the long-term information across views. In this paper, we propose to address the stereo image restoration with transformer by leveraging its powerful capability of modeling long-range context dependencies. Specifically, we construct a stereo image restoration transformer (SIR-Former) to effectively exploit the cross-view correlations. First, to explore the global correspondence for view alignment effectively, we devise a stereo alignment transformer (SAT) module across stereo images, enabling robust alignment under the epipolar constraint. Then, we design a stereo fusion transformer (SFT) module for aggregating the cross-view information in a small horizontal neighborhood, aiming to enhance important features for succeeding restoration. Extensive experiments show that SIR-Former can remarkably boost quantitative and qualitative quality on various image restoration tasks (e.g., super-resolution, deblurring, deraining, and low-light enhancement), which demonstrate the effectiveness of the proposed framework.

Structure and texture information is critically important for low-light image enhancement, in terms of stable global adjustment and fine details recovery. However, most existing methods tend to learn the structure and texture of low-light images in a coupled manner, without well considering the heterogeneity between them, which challenges the capability of the model to learn both adequately. In this paper, we tackle this problem in a divide and conquer strategy, based on the observation that the structure and texture representations are highly separated in the frequency spectrum. Specifically, we propose a Structure and Texture Aware Network (STAN) for low-light image enhancement, which consists of a structure sub-network and a texture sub-network. The former exploits the low-pass characteristic of the transformer to capture low-frequency-related structural representation. While the latter builds upon central difference convolution to capture high-frequency-related texture representation. We establish the Multi-Spectrum Interaction (MSI) module between two sub-networks to bidirectionally provide complementary information. In addition, to further elevate the capability of the model, we introduce a dual distillation scheme that assists the learning process of two sub-networks via counterparts' normal-light structure and texture representations. Comprehensive experiments show that the proposed STAN outperforms the state-of-the-art methods qualitatively and quantitatively.

Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal modeling and then average multiple snippet-level predictions to yield the final video-level prediction. Thus, their video-level prediction does not consider spatio-temporal features of how video evolves along the temporal dimension. In this paper, we introduce a novel Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. To be more specific, we attempt to generate a dynamic kernel for a convolutional operation to aggregate long-range temporal information among adjacent snippets adaptively. The DSA module is an efficient plug-and-play module and can be combined with the off-the-shelf clip-based models (i.e., TSM, I3D) to perform powerful long-range modeling with minimal overhead. The final video architecture, coined as DSANet. We conduct extensive experiments on several video recognition benchmarks (i.e., Mini-Kinetics-200, Kinetics-400, Something-Something V1 and ActivityNet) to show its superiority. Our proposed DSA module is shown to benefit various video recognition models significantly. For example, equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved from 74.9% to 78.2% on Kinetics-400. Codes are available at https://github.com/whwu95/DSANet.

Stereo Video Super-Resolution (StereoVSR) aims to generate high-resolution video steams from two low-resolution videos under stereo settings. Existing video super-resolution and stereo image super-resolution techniques can be extended to tackle the StereoVSR task, yet they cannot make full use of the multi-view and temporal information to achieve satisfactory performance. In this paper, we propose a novel Stereo Video Super-Resolution Network (SVSRNet) to fulfill the StereoVSR task via exploiting view-temporal correlations. First, we devise a view-temporal attention module (VTAM) to integrate the information of cross-time-cross-view for constructing high-resolution stereo videos. Second, we propose a spatial-temporal fusion module (STFM), which aggregates the information across time in intra-view to emphasize important features for subsequent restoration. In addition, we design a view-temporal consistency loss function to enforce consistency constraint of superresolved stereo videos. Comprehensive experimental results demonstrate that our method generates superior results.