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.

Detecting driver fatigue is critical to ensuring road safety. Existing fatigue detection methods typically rely on traditional hand-picked features as inputs. However, these hand-picked features can hardly respond accurately to the driver’s fatigue state due to a certain degree of subjectivity and the extraction of these features requires a long time window, which limits the accuracy and real-time performance of the detection. This paper proposes a novel fatigue detection method based on multi-source information fusion, which relies entirely on neural networks for automatic feature extraction. Through simulated driving experiments, we recorded physiological signals and facial videos from 21 participants for model training and testing. The results show that our model outperforms existing methods in terms of accuracy and real-time performance, achieving a detection accuracy of 93.15% within a 3-second time window (specificity = 94.04%, sensitivity = 91.71%). The visualization results of the model reveal potential relationships between facial regions for the first time, validating the rationality and effectiveness of our method. The practical issues of fatigue detection methods and future research directions are also explored.

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.

Captured images of outdoor scenes usually exhibit low visibility in cases of severe haze, which interferes with optical imaging and degrades image quality. Most of the existing methods solve the single-image dehazing problem by applying supervised training on paired images; however, in practice, the pairing of real-world images is not viable. Additionally, the processing speed of individual dehazing models is important in practical applications. In this study, a novel unsupervised single image dehazing network (USID-Net) based on disentangled representations without paired training images is explored. Furthermore, considering the trade-off between performance and memory storage, a compact multi-scale feature attention (MFA) module is developed, integrating multi-scale feature representation and attention mechanism to facilitate feature representation. To effectively extract haze information, a mechanism referred to as OctEncoder is designed to include multi-frequency representations that can capture more global information. Extensive experiments show that USID-Net achieves competitive dehazing results and a relatively high processing speed compared to state-of-the-art methods. The source code is available athttps://github.com/dehazing/USID-Net.