Adverse weather conditions can significantly degrade image quality and impair the capture of critical information. Existing restoration networks struggle to effectively combine local, regional, and global features, thereby limiting their ability to handle diverse impacts of such weather. This study proposes the local-region-global transformer (LRGFormer), a transformer-based image restoration model for multiscale feature perception. The model comprises a basic module composed of multi-scale fusion attention (MSFSA) and a channel-spatial dual-attention feed-forward network (CSDF). Specifically, this study designs an MSFSA module. For the first time, it combines rotation-equivariant convolution with local attention for local information extraction and introduces a frequency-domain adaptive attention mechanism. By incorporating a query-aware global adaptive sparse attention mechanism for global information extraction, the network gradually fuses along the channel dimension, enabling progressive capture of spatial and frequency-domain information from the local and regional to global scale. Secondly, a CSDF network structure was designed to enhance channel-spatial interaction and improve the representational capacity of the model. By constructing a basic U-Net framework, the excellent basic modules for image restoration proposed in recent years are compared on a unified framework. Experimental results demonstrated that the proposed basic module can not only better extracts multi-scale features of images and restores image distortion caused by various degradation factors, and also exhibits good universality and generalization.

Images suffer from color shift and detail distortion owing to limitations of contrast and visibility in hazy scenes, affecting their subjective perception. However, the performance of existing algorithms on real-world hazy images remains limited as scenes can be complex and haze degradation varies in outdoor visual systems. This study proposes a meta-knowledge single image dehazing algorithm based on hierarchical decoupling, combining the advantages of convolutional neural networks (CNNs) and transformers. We propose a novel dual-branch decoupling network that decouples low-level features from high level semantic information in images, leveraging the hierarchical properties of the network. It combines a CNN and cross dual branch transformer network (CDual transformer) in the encoder network to fully extract local and global features of images. To disentangle high-level semantic features, a style transfer module is designed to transform the style of hazy images while retaining the remaining semantic information. Afterward, the low-level features of images and the transformed high-level semantic features are used to reconstruct the dehazed images, fully capitalizing on the multilevel features. Furthermore, we built a meta-semi-supervised training strategy to improve the decoupling performance of the model and accumulated style knowledge of clear images from both synthetic and real-world hazy data, improving the generalizability of the model. Extensive experiments on both synthetic and real datasets show that the proposed algorithm effectively removes haze and offers better generalization abilities than similar methods. The project is publicly available at https://github.com/BJUTsipl/HDMDN.

Adverse weather and imaging environments may degrade image quality and pose a significant challenge to the visual perception systems of multimedia. Various image restoration tasks necessitate the modeling of multiscale features, which is highly demanding on networks. To date, Vision Transformer has exhibited impressive image restoration performance. However, in this model, global self-attention is computationally expensive and local self-attention typically limits the interaction domain of each token. To solve this problem, we propose a novel reconfigurable self-attention transformer called RcFormer, which is designed to adequately model multiscale image features. This is achieved through a cross-grouped transformer (CGTransformer) block that uses convolution, area self-attention, and row-column self-attention for different head groups. CGTransformer is combined with an intragroup operation interaction structure. Moreover, an intergroup reconfigurable mechanism is implemented based on CGTransformer and channel circulation. The combination of multiple operations effectively enhances the modeling capability in the spatial and channel dimensions for various image recovery tasks. The performance of the proposed RcFormer is compared with low-level vision modules in a unified framework. Extensive experiments demonstrated that RcFormer exhibited a superior performance for the following image restoration tasks: image dehazing, rain streak removal, raindrop removal, snow removal, and single image deblurring. The source code is publicly available athttps://github.com/dehazing/RcFormer.

Captured outdoor scene images are easily affected by haze. Most image dehazing methods have limited generalization capabilities for real-world hazy images owing to the complexities of real-world environments and domain gaps in the training datasets. This article proposes a semi-supervised single-image dehazing network based on disentangled meta-knowledge. The symmetric and heterogeneous design of the disentangled network is conducive to the separation of the content and mask features of hazy images and these features are used as meta-knowledge to guide feature fusion in the dehazing network. Moreover, functions describing constant-color and disentangled-reconstruction-checking losses are designed to ensure the subjective qualities of the generated dehazed images. The results of extensive experiments conducted on synthetic datasets and real-world images indicate that the proposed algorithm outperforms state-of-the-art single-image dehazing algorithms. In addition, the algorithm effectively improves the performance of object-detection tasks.

Haze seriously affects the reliability of industrial systems, especially vision-based outdoor industrial systems such as autopilot systems. A majority of existing dehazing methods are not specifically designed for industrial systems and do not consider the reliability and resource cost of industrial system implementation. In this article, a novel meta-attention dehazing network (MADN) is proposed for direct restoration of clear images from hazy images without using the physical scattering model. Combined with parallel operation and enhancement modules, the meta-network automatically selects the most suitable dehazing network structure based on the current input hazy image by a meta-attention module. In addition, a novel feature loss calculated by the meta-network is proposed, which can accelerate the convergence of the dehazing network to meet the application requirements of practical industrial systems. A large number of experimental results on synthetic and real-world datasets show that the proposed MADN satisfies the needs of industrial systems.