Recently, incorporating Retinex theory with unfolding networks has attracted increasing attention in the low-light image enhancement field. However, existing methods have two limitations, i.e., ignoring the modeling of the physical prior of Retinex theory and relying on a large amount of paired data. To advance this field, we propose a novel self-supervised unfolding network, named S2UNet, for the LIE task. Specifically, we formulate a novel optimization model based on the principle that content-consistent images under different illumination should share the same reflectance. The model simultaneously decomposes two illumination-different images into a shared reflectance component and two independent illumination components. Due to the absence of the normal-light image, we process the low-light image with gamma correction to create the illumination-different image pair. Then, we translate this model into a multi-stage unfolding network, in which each stage alternately optimizes the shared reflectance component and the respective illumination components of the two images. During progressive multi-stage optimization, the network inherently encodes the reflectance consistency prior by jointly estimating an optimal reflectance across varying illumination conditions. Finally, considering the presence of noise in low-light images and to suppress noise amplification, we propose a self-supervised denoising mechanism. Extensive experiments on nine benchmark datasets demonstrate that our proposed S2UNet outperforms state-of-the-art unsupervised methods in terms of both quantitative metrics and visual quality, while achieving competitive performance compared to supervised methods. The source code will be available at https://github.com/J-Liu-DL/S2UNet.

In the field of compressed sensing magnetic resonance imaging (CS-MRI), deep unfolding networks (DUNs) achieve high interpretability and superior performance. However, existing DUN-based methods often treat different components of the MR image uniformly without considering their respective unique characteristics, leading to insufficient detail capture and suboptimal performance. To address this issue, we propose a Dual-BrancH Deep Unfolding Network (DBH-Net), which employs parallel under-complete (UC) and over-complete (OC) branches to alternately reconstruct different components from the under-sampled MR image. The UC branch focuses on extracting low-frequency features by expanding the receptive field, while the OC branch emphasizes high-frequency features by restricting the receptive field. Besides the independent descriptive abilities of dual-branch, the unique characteristics of DUN facilitate a tighter integration between the two branches. Additionally, we introduce an Auxiliary Information Fusion Block (AIFB) to transfer multi-channel auxiliary information between stages, effectively reducing information loss. Extensive experiments on three datasets demonstrate that our proposed DBH-Net outperforms existing state-of-the-art methods.

Low-light image enhancement (LIE) aims to restore images taken under poor lighting conditions, thereby extracting more information and details to robustly support subsequent visual tasks. While past deep learning (DL)-based techniques have achieved certain restoration effects, these existing methods treat all samples equally, ignoring the fact that difficult samples may be detrimental to the network's convergence at the initial training stages of network training. In this paper, we introduce a self-paced learning (SPL)-based LIE method named SPNet, which consists of three key components: the feature extraction module (FEM), the low-light image decomposition module (LIDM), and a pre-trained denoise module. Specifically, for a given low-light image, we first input the image, its pseudo-reference image, and its histogram-equalized version into the FEM to obtain preliminary features. Second, to avoid ambiguities during the early stages of training, these features are then adaptively fused via an SPL strategy and processed for retinex decomposition via LIDM. Third, we enhance the network performance by constraining the gradient prior relationship between the illumination components of the images. Finally, a pre-trained denoise module reduces noise inherent in LIE. Extensive experiments on nine public datasets reveal that the proposed SPNet outperforms eight state-of-the-art DL-based methods in both qualitative and quantitative evaluations and outperforms three conventional methods in quantitative assessments.

Low-light image enhancement (LIE) is important for many high-level vision tasks as the poor visibility of underexposed images can severely degrade the performance of the subsequent image recognition, analysis, etc. Although recent deep-learning-based LIE methods exhibit promising performance, most of them require a large number of paired training images, thereby limiting the practicability to real scenarios. In this paper, we propose a pseudo-supervised LIE method with the integration of mutual learning. Specifically, for the given low-light image, we first use a quadratic curve to generate a pseudo-clear image, which is served as the auxiliary ground truth for supervision, then the pseudo-paired images are simultaneously input to two parallel homogeneous branches to learn the expected enhanced result through the knowledge distillation of two branches via mutual learning. As both the generated image and the input low-light image underlies the desired solution, the mutual learning strategy enables the two branches learn from each other and produce the final results. Extensive experiments demonstrate that the proposed method outperforms most existing unsupervised LIE methods in terms of both qualitative and quantitative evaluations, and also achieves competitive performance against many supervised and semi-supervised methods.

Perceptual image quality is related to content and distortion. Distortion classification is a common way to learn distortion information. How to extract distortion information consistent with human perception is a problem to be solved. Besides, the joint effect on image quality caused by the interplay of content and distortion has not been fully studied. In this paper, a novel Content Distortion Interaction Network (CDINet) is proposed for blind image quality assessment. Distortion representation are guided by content representation to learn quality-aware representation. CDINet consists of four components: a Distortion-Aware Module (DAM), a Content-Aware Module (CAM), an Asymmetric Content-Distortion Interaction (ACDI) module, and a quality regression module. The content representation and distortion representation are extracted respectively and fused interactively in CDINet. Specifically, with the assistance of image restoration, distortion representation consistent with human perception is learned. To further improve the ability in distortion representation, the DAM is used to construct the differences between the distorted image and its reference image. The proposed ACDI module enables the interaction of content and distortion representations to occur at different levels with less computational cost. Since the proposed CDINet considers the joint impact on image quality caused by the interplay of content and distortion, the predicted image qualities highly align with human perception. Comprehensive experiments on 8 benchmark datasets demonstrate that the proposed CDINet effectively extracts quality-aware representation, achieving state-of-the-art performance in evaluating both synthetically and authentically distorted images.

For human beings, there is a natural preference for judging the relative quality rather than directly predicting the quality score of an image. Based on this view, we propose an image quality difference learning network (IQDLNet) for evaluating image quality in a no-reference manner. Specifically, the proposed IQDLNet consists of a quality difference-aware network (QDAN) and a quality assessment network (QAN). The QDAN aims to predict the score difference between two randomly matched images and the QAN aims to predict the quality score of these two images. To further enhance the mutual understanding of image semantics, a semantic interaction module (SIM) is proposed with a dual regressor set up to carry out competitive learning in combination with the quality difference-aware feature. Experimental results on five IQA datasets demonstrate the superior performance of the proposed method over eight state-of-the-arts.

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical diagnosis. However, it is time-consuming to obtain MR data of multi-contrasts and the long scanning time may bring unexpected physiological motion artifacts. To obtain MR images of higher quality within limited acquisition time, we propose an effective model to reconstruct images from under-sampled k-space data of one contrast by utilizing another fully-sampled contrast of the same anatomy. Specifically, multiple contrasts from the same anatomical section exhibit similar structures. Enlightened by the fact that co-support of an image provides an appropriate characterization of morphological structures, we develop a similarity regularization of the co-supports across multi-contrasts. In this case, the guided MRI reconstruction problem is naturally formulated as a mixed integer optimization model consisting of three terms, the data fidelity of k-space, smoothness-enforcing regularization, and co-support regularization. An effective algorithm is developed to solve this minimization model alternatively. In the numerical experiments, T2-weighted images are used as the guidance to reconstruct T1-weighted/T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) images and PD-weighted images are used as the guidance to reconstruct PDFS-weighted images, respectively, from their under-sampled k-space data. The experimental results demonstrate that the proposed model outperforms other state-of-the-art multi-contrast MRI reconstruction methods in terms of both quantitative metrics and visual performance at various sampling ratios.

Owing to the rapid development of deep networks, single image deraining tasks have achieved significant progress. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed by existing deraining methods. However, many of them cause a loss of details during deraining, resulting in visual artifacts. To resolve the detail-losing issue, we propose a novel unrolling rain-guided detail recovery network (URDRN) for single image deraining based on the observation that the most degraded areas of the background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learning-based methods trivialize the observation model and simply learn an end-to-end mapping, the proposed URDRN unrolls the single image deraining task into two subproblems: rain extraction and detail recovery. Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks, and then, a rain attention map is generated as an indicator to guide the detail-recovery process. For a detail-recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details. Experiments on several well-known benchmark datasets show that the proposed approach can achieve a competitive performance in comparison with other state-of-the-art methods.

In this paper, we propose a cross-channel, cross-scale, and cross-stage network (C3Net) for single image super-resolution, which effectively shares the features learned from multiple channels, multiple scales, and multiple stages. Multi-scale spatial features are extracted in each stage in an encoder-decoder fashion. The channel attention is performed after each encoder to exploit the inter-channel dependencies. After that, we design a cross-stage and cross-scale feature sharing module to accelerate the feature sharing across different scales and different stages. The whole network is optimized by multiple similar stages to reduce the number of parameters. Finally, super-resolution images of multiple resolutions are reconstructed simultaneously. We evaluate the proposed network on four benchmark datasets by comparing it with eleven state-of-the-art methods. Comprehensive experiments show the proposed network outperforms state-of-the-art methods by fewer parameters. The source code is available at https://github.com/thinkerww/SR_Version.

Abstract Compared with video de‐raining, single image de‐raining is more technically difficult due to the lack of temporally redundant information. This paper proposes a new two‐stage method for single image de‐raining. In the first stage, the authors develop an effective two‐step model to detect the rain streaks by taking pixel intensity, and direction of rain streaks as priors. In the second stage, the rain repair process is performed at the patch level. The authors first define a way to search for similar patches of each patch, and then group the similar patches together to form a matrix. Finally, a low‐rank matrix completion technique is utilized to recover the rain‐stained pixels based on the rain map obtained from the first stage. Compared with several state‐of‐the‐art methods, authors' proposed method is competitive in terms of the abilities of removing rain streaks, and preserving image details.

Ciphertext policy attribute-based encryption (CP-ABE) is an encryption mechanism that can provide fine-grained access control and adequate cloud storage security for Internet of Things (IoTs). In this field, the original CP-ABE scheme usually has only a single trusted authority, which will become a bottleneck in IoTs. In addition, different users may illegally share their private keys to obtain improper benefits. Besides, the data owners also require the flexibility to change their access policy. In this paper, we construct a multiauthority CP-ABE scheme on prime order groups over a large attribute universe. Our scheme can support white-box traceability along with policy updates to solve the abovementioned three problems and, thus, can fix the potential requirements of IoTs. More precisely, the proposed scheme supports multiple authority, white box traceability, large attribute domains, access policy updates, and high expressiveness. We prove that our designed scheme is static secure and traceable secure based on the state-of-the-art security models. Moreover, by theoretical comparison, our scheme has better performance than other schemes. Finally, extensive experimental comparisons show that our proposed algorithm can be better than the baseline algorithms.

Abstract In this paper, we proposed an idea to construct a general multivariate public key cryptographic (MPKC) scheme based on a user’s identity. In our construction, each user is distributed a unique identity by the key distribution center (KDC) and we use this key to generate user’s private keys. Thereafter, we use these private keys to produce the corresponding public key. This method can make key generating process easier so that the public key will reduce from dozens of Kilobyte to several bits. We then use our general scheme to construct practical identity-based signature schemes named ID-UOV and ID-Rainbow based on two well-known and promising MPKC signature schemes, respectively. Finally, we present the security analysis and give experiments for all of our proposed schemes and the baseline schemes. Comparison shows that our schemes are both efficient and practical.

Email has become a standard communication method nowadays. But when it comes to important correspondences containing sensitive governmental, commercial or medical information, certified delivery is required. We present in this paper a new certified email protocol which assures fair delivery, non-repudiability, confidentiality and timeliness. To achieve definite fairness, an offline TTP is employed which will be involved only if there is a dispute.