Existing one-time dehazing approaches struggle to simultaneously restore high-fidelity scene content and suppress haze-induced artifacts. Although recursive iterations can enhance the modeling capacity for complex degradations, the associated estimation errors are often amplified during propagation, thereby constraining the overall dehazing performance. To address these challenges, this work proposes a deep unfolding dehazing network, termed I3-Net, which integrates an iterative refinement strategy (IRS) and self-prompted correction learning (SCL). Specifically, we first develop a baseline dehazing network, termed I-Net, which is constructed around a physically-aware feature enhancement module (PFEM). By embedding physical priors into the feature space, PFEM enforces consistency between learned representations and the haze degradation process, thereby providing a reliable foundation for subsequent refinement. Building upon I-Net, IRS is designed to recursively unfold the baseline, progressively improving its dehazing outputs and enabling the construction of a deep unfolding dehazing network, I3-Net, with cross-stage feature association capability. To mitigate the accumulation of minor estimation errors inherent in iterative frameworks, we further propose SCL mechanism inspired by the corrective behavior of the human visual system. By integrating IRS and SCL, I3-Net adaptively identifies and rectifies residual haze regions at each unfolding stage, effectively suppressing error propagation and achieving high-quality image restoration. Extensive experiments demonstrate that the proposed I3-Net consistently outperforms existing SOTA methods in both quantitative metrics and visual perception across multiple benchmark datasets.

This paper investigates one of the most challenging problems in single image dehazing: how to restore haze-free scenes solely from the input observed image without relying on paired or unpaired images and how to extract useful prior information from the observed image to guide the dehazing process. To address these challenges, this paper introduces a novel zero-reference real-world image dehazing method via deep self-decoupling and reverse knowledge transfer (ZRID-Net). Specifically, we first employ a model-driven approach to preliminarily decouple the observed image into coarse-grained components: the haze-free image, transmission map, and atmospheric light. Subsequently, we refine the haze-free image and transmission map separately via a data-driven approach. In addition, we propose a novel reverse knowledge transfer method to exploit latent prior information within hazy images thoroughly for dehazing guidance. This method combines knowledge transfer and contrastive learning to reverse guide the refinement network away from haze characteristics. Finally, a perceptual fusion strategy is employed to obtain haze-free images with high visibility and realism. Extensive experiments demonstrate that the proposed ZRID-Net effectively restores image clarity, enhances structural details, and improves color fidelity across various challenging haze conditions without relying on paired or unpaired supervision. On multiple benchmark datasets, ZRID-Net outperforms existing SOTA approaches in terms of both quantitative metrics and visual quality. The results also confirm its strong generalizability and practical applicability to real-world scenarios. The relevant implementation code can be found at https://github.com/cswangshilong/ZRID-Net.

Existing image dehazing methods are largely constrained to spatial domain processing, failing to fully leverage the rich knowledge embedded in the frequency domain of clear images. Additionally, the traditional convolutional operations in network architectures limit their mapping capabilities to some extent. To address these issues, a novel image dehazing network, termed the Detail and Content Enhancement Network (DCE-Net), is proposed. DCE-Net redefines dehazing task from a frequency-domain perspective, incorporating differential convolution and attention mechanisms to design the High-Frequency Detail Enhancement Module (HDEM) and the Low-Frequency Content Enhancement Module (LCEM). Furthermore, a Dual-Frequency Domain Knowledge-Guided Strategy (DDKS) is introduced during the training phase to exploit the abundant frequency-domain priors inherent in clear images. Experimental results demonstrate that the DCE-Net achieves outstanding performance on both synthetic benchmark datasets and real-world hazy scenes. DCE-Net not only significantly restores image clarity and contrast but also effectively preserves details and content features.

Abstract Texture and color distortion are common in existing learning‐based dehazing algorithms, and it is argued that one of the major reasons is that the shallow features of fog images are underutilized, and the deep features of fog images are insufficient for single image dehazing. In order to provide more texture and color information for image restoration, more shallow features need to be added in the process of image decoding. Therefore, a multi‐scale feature fusion pyramid attention network (PAN) for single image dehazing is proposed. In PAN, combined with the attention mechanism, a shallow and deep feature fusion (SDF) strategy is designed. SDF considers multi‐scale as well as channel‐level fusion to provide feature information under different receptive fields while also highlighting important channels, such as texture and color information. DC is designed as a latent space mapping module to learn a mapping relationship between the latent space representation of the hazy image at low resolution and the corresponding latent space representation of the haze‐free image. Additionally, network deconvolution (ND) and deformed convolution network (DCN) are introduced into PAN. The ND module can remove pixel‐wise and channel‐wise correlation of features, reduce data redundancy to obtain sparse representation of features, and speed up network convergence. The DCN module can use its adaptive receptive field to focus on the area of interest for calculation and play a role in texture feature enhancement. Finally, the perceptual loss is chosen as the regularization item of the loss function, which makes style features of the restored image closer to the real fog‐free image. Extensive experiments reveal that the proposed PAN outperforms other existing dehazing methods on real‐world and synthetic datasets.

Recently, part-based person re-identification methods attract lots of attention and largely improve the accuracy. However, due to the large variations in camera occlusion, pose change and misalignment, the corresponding part regions of different images from a same person may miss the key cues. In this paper, we proposed a local to global with multi-scale attention network (LGMANet), which sufficiently exploits the contextual information and spacial attention information. Our proposed model includes two branches. One is local to global branch. By pooling operation, an image generates the feature maps of different dimensions. Then, we learn local to global descriptors by partitioning these feature maps with the same scale. The other is multi-scale attention branch, which captures the contextual dependencies from different convolution layers and further improves the discriminative ability of the image feature. Experimental results demonstrate that our method achieves the state-of-the-art results on three benchmark datasets, Market-1501, DukeMTMC-reID and CUHK03.

Most existing methods on person re-identification ignore contextual dependencies which are important in representing pedestrian images. In this paper, we propose a Multi-Branch Context-Aware Network (MBCAN) for person re-identification to exploit rich context information. MBCAN learns global features and local-part features in two separate branches to take full advantages of both coarse-grained and fine-grained features. Additionally, two types of attention modules are introduced to capture contextual dependencies in spatial dimension and channel dimension, respectively. A module called feature vector extraction block is designed to find an efficient way to integrate features from coarse to fine. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on Market-1501, DukeMTMC-reID and CUHK03 datasets.

Most existing methods on person re-identification pay redundant attention to global features or local features which ignore contextual dependencies which are equally important in representing pedestrian images. In this paper, we propose a Multi-Branch Context-Aware Network (MBCAN) for person re-identification to exploit rich context information. MBCAN learns global features and local-part features in two separate branches to take full advantages of both coarse-grained and fine-grained features. Additionally, two types of attention modules are introduced to capture contextual dependencies in spatial dimension and channel dimension, respectively. A module called feature vector extraction block is designed to find an efficient way to integrate features from coarse to fine. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on Market-1501, DukeMTMC-reID and CUHK03 datasets.

In order to perform complex data processing and co-simulation workflows for research on data driven energy systems, a generic, modular and highly scalable process operation framework is presented in this article. This framework consistently applies web technologies to build up a microservices architecture. It automates the startup, synchronization, and management of scientific data processing and simulation tools (e.g. Python, Matlab, OpenModelica) as part of larger transdisciplinary, multi-domain data processing and co-simulation workflows. It uses container virtualization on the underlying cluster computing environment to control and manage different simulation nodes.Within the framework's processing workflow, software executables can be distributed to different nodes on the cluster, easily access data and communicate with other components via communication adapters and a high-performance messaging channel infrastructure. By integrating Apache NiFi, the framework also provides an easy-to-use web user interface to allow users to model, perform and operate workflows for future energy system solutions. As soon as a complex workflow is set up in the process operation framework, researchers can use the workflow without any setup or configuration on their local workstations and without knowing any details of the underlying infrastructure or software environment.

In recent years, video-based person re-identification has attracted more and more attention. However, most existing video-based methods do not fully consider the intrinsic structure and invariant information of the same person across different cameras. In this paper, we propose a graph regularized and label-matched dictionary learning (GRLDL) method to capture the intrinsic structure of the same person between two cameras. Firstly, in order to reduce the variations between different cameras, we use local Fisher discriminant analysis to transform the person videos from different cameras into a common feature space. A dictionary is learned from this common space. Then, we construct a graph regularization term to preserve the geometrical structure of the same person and enhance the discriminative ability of the learned dictionary. Finally, a projective matrix is introduced to map the coding coefficients into a label space, which is able to correlate and match the same person under different cameras. Experiments on the public iLIDS-VID and PRID 2011 datasets show the effectiveness of the proposed method.

The existing visibility distance estimation algorithms in foggy situations use the region growing method to extract the vertical position of inflection point of image intensity changing. These algorithms have lower inflection point location accuracy for an image with a non‐homogeneous road surface. To deal with these problems, this study presents a novel visibility distance measuring technique under foggy weather conditions. This method combines two major models: inflection point estimation (IPE) model and transmission refining (TR) model. The proposed IPE model based on transmission computation model derives a very useful relation between the transmission value of inflection points and the constant . In order to acquire the more accurate transmission map and vertical position of each inflection point, this study establishes an effective TR model. This model exploits the edge information of input images, in order to significantly reduce the effects of artefact. The proposed algorithm provides more accurate visibility distance estimation of an image with a non‐homogeneous road surface than the well‐known algorithm through qualitative evaluations in experiments. The experimental results also show that the TR model has better outcomes than the guided filter approach through qualitative and quantitative evaluations.