RGB-to-RAW reconstruction, or the reverse modeling of a camera Image Signal Processing (ISP) pipeline, aims to recover high-fidelity RAW data from RGB images. Despite notable progress, existing learning-based methods typically treat this task as a direct regression objective and still struggle with detail inconsistency and color deviation, due to the ill-posed nature of inverse ISP and the inherent information loss in quantized RGB images. To address these limitations, we pioneer a generative perspective by reformulating RGB-to-RAW reconstruction as a deterministic latent transport problem and introduce a novel framework named RAW-Flow, which leverages flow matching to learn a deterministic vector field in latent space, to effectively bridge the gap between RGB and RAW representations and enable accurate reconstruction of structural details and color information. To further enhance latent transport, we introduce a cross-scale context guidance module that injects hierarchical RGB features into the flow estimation process. Moreover, we design a Dual-domain Latent Autoencoder (DLAE) with a feature alignment constraint to support the proposed latent transport framework, which jointly encodes RGB and RAW inputs while promoting stable training and high-fidelity reconstruction. Extensive experiments demonstrate that RAW-Flow outperforms state-of-the-art approaches both quantitatively and visually.

Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a robust framework for structural damage detection coupled with a micro drone. DetectorX addresses the challenges of object detector robustness by incorporating two innovative modules: a stem block and a spiral pooling technique. The stem block introduces a dynamic visual modality by leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models. The framework employs the proposed event-based reward reinforcement learning to constrain the actions of a parent and child DCNN model leading to a reward. This results in the induction of two dynamic visual modalities alongside the Red, Green, and Blue (RGB) data. This enhancement significantly augments DetectorX's perception and adaptability in diverse environmental situations. Further, a spiral pooling technique, an online image augmentation method, strengthens the framework by increasing feature representations by concatenating spiraled and average/max pooled features. In three extensive experiments: (1) comparative and (2) robustness, which use the Pacific Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment, DetectorX performed satisfactorily across varying metrics, including precision (0.88), recall (0.84), average precision (0.91), mean average precision (0.76), and mean average recall (0.73), compared to the competing detectors including You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate that DetectorX can provide satisfactory results and demonstrate resilience in challenging environments.

Existing SISR (single image super-resolution) methods mostly assume that a low-resolution (LR) image is bicubicly down-sampled from its high-resolution (HR) counterpart, which inevitably give rise to poor performance when the degradation is out of assumption. To address this issue, we propose a framework PRED (parallel residual and encoder-decoder network) with an innovative training strategy to enhance the robustness to multiple degradations. Consequently, the network can handle spatially variant degradations, which significantly improves the practicability of the proposed method. Extensive experimental results on real LR images show that the proposed method can not only produce favorable results on multiple degradations, but also reconstruct visually plausible HR images.

Screen content videos (SCVs) are becoming popular in many applications. Compared with natural content videos (NCVs), the SCVs have different characteristics. Therefore, the screen content coding (SCC) based on HEVC adopts some new coding tools (intra block copy and palette mode etc.) to improve coding efficiency, but these tools increase the computational complexity as well. In this paper, we propose to predict the CU partition of the SCVs by a convolutional neural network (CNN) which is trained by the large-scale database that we firstly established for screen content coding. The proposed approach is implemented in SCC reference software SCM-6.1. Experimental results show that our proposed approach can save 53.2% encoding time with 2.67% BD-rate increase on average in All Intra (AI) configurations.

Given multiple texture projection methods from the sphere surface to the planar surface, this paper proposes an adaptive selection mode that automatically chooses the appropriate projection method to obtain high compression efficiency of the VR video. The video compression efficiency is inherently affected by the video content, which is closely related to the projection method in the case of VR video encoding. In order to represent the VR video content in a compact manner, a feature vector (transferable feature) for each frame is extracted by a Res-CNN which is pre-trained by a large scale data set for general classification. Afterwards, the relation between the feature and the optimal projection method is investigated by using PCA-KNN, which can project the initial feature vector to a subspace where the VR videos can be efficiently classified with low ambiguity. The experimental results show that the proposed method can select the appropriate projection method that generates the best BD rate.

The image down-conversion may be used in the block-based image compression because it can help save lots of bit-counts for each individual block. A straightforward way to implement the transform domain down-conversion is to truncate some high-frequency components to get a down-sized coefficient block. However, directly using this down-sized coefficient block to reconstruct a completed image block will lead to a serious quality degradation. In this paper, we propose a constrained quantization based transform domain down-conversion (CQTDD) to help compress each 16×16 macro-block and it makes the coding quality of 1/4 selected pixels (according to a regular pattern) in each macro-block much higher than that can be achieved by using the traditional truncation based approach. Meanwhile, the other 3/4 pixels will be interpolated by using those 1/4 well-reconstructed pixels. Furthermore, these 1/4 pixels are optimized before the compression to help get a more efficient interpolation. Finally, the proposed CQTDD works with the JPEG baseline coding together as two candidate coding modes in our proposed compression scheme. Experimental results demonstrate that our proposed method may offer a remarkable quality gain, both objectively and subjectively, compared with some existing methods.

In this paper, we design an interpolation-directed transform domain downward conversion (ITDDC) to build up a new block-based image compression scheme. This ITDDC is derived from our proposed 2-D padding and performed on each 16×16 macro-block of pixels to convert it into an 8×8 coefficient block, leading to a downward image conversion in the transform domain. More interestingly, the further compression is just performed on the down-sized coefficient block and the reconstruction for an entire macro-block is achieved via the interpolation by using the decoded pixels only locating in some specific positions of it. To make the interpolation more efficient, the pixels participating in the interpolation will be optimized before the compression. The ITDDC-based coding is used competitively with the JPEG baseline coding to compress each macro-block in our proposed compression scheme according to a simple but efficient rate-distortion optimization based criterion. Experimental results demonstrate that our proposed method gets a remarkable quality gain over the existing approaches.

Image interpolation offers an efficient way to compose a high-resolution (HR) image from the observed low-resolution (LR) image. Advanced interpolation techniques design the interpolation weighting coefficients by solving a minimum mean-square-error (MMSE) problem in which the local geometric similarity is often considered. However, using local geometric similarities cannot usually make the MMSE-based interpolation as reliable as expected. To solve this problem, we propose a robust interpolation scheme by using the nonlocal geometric similarities to construct the HR image. In our proposed method, the MMSE-based interpolation weighting coefficients are generated by solving a regularized least squares problem that is built upon a number of dual-reference patches drawn from the given LR image and regularized by the directional gradients of these patches. Experimental results demonstrate that our proposed method offers a remarkable quality improvement as compared to some state-of-the-art methods, both objectively and subjectively.

Image interpolation refers to constructing a high-resolution (HR) image from a low-resolution (LR) image. Traditionally, an HR image can be produced from an observed LR image via the polynomial-based interpolation (bi-linear or bi-cubic interpolations, involving a small number of neighbors around each interpolated position). The advanced interpolation makes use of the so-called “geometric similarity” to design a set of optimal interpolation weighting coefficients. However, better geometric similarities can perhaps be found from a non-local area within the LR source image or even from other but similar images (possibly with higher resolutions). Based on this fact, we propose in this paper a non-local geometric similarity based interpolation scheme to construct HR images. In our proposed method, optimal weighting coefficients are determined by solving a regularized least squares problem which is built upon a number of dual reference patches drawn from the observed LR image and regularized by the variation of directional gradients of the image patch. Experimental results demonstrate that our proposed method offers a remarkable quality improvement, both objectively and subjectively.

In this paper, we propose a novel hierarchical generative model, named author-genre-topic model (AGTM), to perform satellite image annotation. Different from the existing author-topic model in which each author and topic are associated with the multinomial distributions over topics and words, in AGTM, each genre, author, and topic are associated with the multinomial distributions over authors, topics, and words, respectively. The bias of the distribution of the authors with respect to the topics can be rectified by incorporating the distribution of the genres with respect to the authors. Therefore, the classification accuracy of documents is improved when the information of genre is introduced. By representing the images with several visual words, the AGTM can be used for satellite image annotation. The labels of classes and scenes of the images correspond to the authors and the genres of the documents, respectively. The labels of classes and scenes of test images can be estimated, and the accuracy of satellite image annotation is improved when the information of scenes is introduced in the training images. Experimental results demonstrate the good performance of the proposed method.

In this paper, a novel method is proposed to predict attention in image scenes by using a central stimuli sensitivity based saliency model. The proposed method is based on the general “center-surround” visual attention mechanism and the spatial frequency response of the human visual system (HVS). Following three biologically inspired principles, the saliency value is computed by two “scatter matrices” which are used to measure the similarity and distinctness within and between two classes, i.e., the center and surrounding regions, respectively. In order to detect salient objects with different size, the saliency of a pixel is estimated via the saliency support region of the pixel, which is the most salient region centered at the pixel with respect to the surrounding region. The proposed method which is compliant with human perceptual characteristics enables the prediction of human fixations. Experimental results on three eye tracking datasets verify the effectiveness of the method and show that the proposed method outperforms the state-of-the-art methods on the visual saliency detection task.

In this paper, we propose a novel feature adaptive co-segmentation method that can learn adaptive features of different image groups for accurate common objects segmentation. We also propose image complexity awareness for adaptive feature learning. In the proposed method, the original images are first ranked according to the image complexities that are measured by superpixel changing cue and object detection cue. Then, the unsupervised segments of the simple images are used to learn the adaptive features, which are achieved using an expectation-minimization algorithm combining l 1-regularized least squares optimization with the consideration of the confidence of the simple image segmentation accuracies and the fitness of the learned model. The error rate of the final co-segmentation is tested by the experiments on different image groups and verified to be lower than the existing state-of-the-art co-segmentation methods.

Ultra Wideband (UWB) system uses extremely low signal power to coexist with other primary users (PUs), which limits the use of the UWB's wide spectrum band. Cognitive Radio (CR) enables UWB systems to efficiently use the overlapped spectrum without causing interference to other wireless systems. In this paper, we focuses on the low-complexity joint optimization algorithm design with respect to transmit power allocation and spectrum sensing time (SST) for maximizing the spectrum efficiency of the Orthogonal Frequency Division Multiplexing based CR-UWB system. The SST optimization algorithm minimizes the spectrum sensing time in order to maximize the time length of applying the power allocation algorithm for data transmission. The proposed group power allocation algorithm adaptively assigns the transmit power to the subcarrier groups according to the effective signal-to-noise ratio (SNR) of each subcarrier group based on greedy algorithm. The proposed joint optimization algorithm can maximize the CR-UWB systems spectrum efficiency at a extremely low primary user SNR regime with low complexity.

The saturation throughput analysis of the prioritized contention access (PCA) of the WiMedia Ultra Wideband (UWB) standard is presented in this paper. In the WiMedia standard, the Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) is used as the basic physical scheme. An in-depth Bit Error Rate (BER) performance analysis of the MB-OFDM UWB system using IEEE 802.15.3a channel models is conducted to aid the throughput analysis. The PCA throughput performance analysis is carried out by extending an original Enhanced Distributed Contention Access (EDCA) model for 802.11e into the MB-OFDM UWB protocol. The extended model considers the effects of the bit error rate, the transmission opportunity limits, and the uniqueness of WiMedia MAC timing structure. The results illustrate that the station saturation throughput of the PCA scheme is sensitive to the frame payload length, and there exists an optimal frame payload length that maximizes the saturation throughput. The results also show that the saturation throughput of the Access Category (AC) with higher priority outperforms the lower prioritized AC under all types of BER conditions.