Current camouflaged object detection (COD) methods predominantly focus on cross-layer feature fusion while neglecting cross-channel information interaction within the same layer. To address this limitation, we propose ARNet, a novel channel-aware refinement network featuring three key innovations. First, our Channel Information Interaction Module (CIIM) enables bidirectional horizontal-vertical integration of split-channel features, effectively capturing complementary cross-channel information through channel dimension fusion. Second, the Assisted Guidance Module (AGM) generates semantic-aware guidance maps from backbone features to dynamically regulate feature decoding and enhance spatial localization. Third, a Multi-scale Enhancement (MSE) module employs multi-scale convolution and residual-connected strategies to refine feature representations and expand contextual perception. Extensive experiments on COD10K, CAMO, and NC4K datasets demonstrate ARNet's superiority over 12 state-of-the-art methods. Code and results are available at: https://github.com/akuan1234/ARNet

Camouflaged object detection is designed to segment objects that are highly integrated into the background and is highly challenging. At present, the main problem is how to effectively maintain the consistency of object semantics and avoid the loss of object information in the process of multi-scale feature fusion. To address this issue, we propose an efficient camouflaged object detection network (CHNet) based on channel reconstruction and hybrid attention. In our method, the feature channel is reconstructed using the strategy of Fusion-Separation-Transform-Fusion, and the adjacent features with strong semantic correlation are fused. Then, the global and local hybrid attention mechanism is used for layer-by-layer refinement, to maintain the consistency of object semantics to the largest extent. Experimental results on three large benchmark datasets show that our CHNet outperforms 11 state-of-the-art (SOTA) models, making it the most advanced COD model with 11.19M parameters. Our code is available at https://github.com/akuan1234/CHNet

In sand‐dust environments, light is scattered and absorbed, and sand‐dust images thus suffer from severe image degradation problems, such as color shifts, low contrast, and blurred details. To address these problems, we propose a two‐step unsupervised sand‐dust image enhancement algorithm. In the first step, a convenient and competent color correction method is put forward to solve the color shift problem. Considering the wavelength attenuation features of sand‐dust images, a linear stretching and blue channel compensation method is designed, and an adaptive color shift correction factor is developed to remove the color shift. In the second step, to enhance the clarity and details of the images, an unsupervised generative adversarial network is proposed, which does not require pairs of data for training. To reduce detail loss, the detail enhancement branch is designed, and the generator considers to more details through the constructed coarse‐grained and fine‐grained discriminators. The introduced multiscale perceptual loss promotes the image fidelity well. Experiments show that the proposed method achieves better color correction, enhances image details and clarity, has a better subjective effect, and outperforms existing sand‐dust image enhancement methods both quantitatively and qualitatively. Similarly, our method promotes the application capability of the target detection algorithm and also has a good enhancement effect on underwater images and haze images.