Key points are not available for this paper at this time.
Image denoising is a quintessential challenge in computer vision, intending to produce high-quality, clean images from degraded, noisy counterparts. Infrared imaging holds a pivotal position across many research domains, attributed to its inherent benefits such as concealment and noninvasiveness. Despite these advantages, infrared images are often plagued by hardware-related imperfections resulting in poor contrast, diminished quality, and noise contamination. Extracting and characterizing features amidst these unique feature patterns in infrared imagery are taxing tasks. To surmount these obstacles, we introduce the Infrared image Denoising Transformer (IDTransformer), encapsulated in a symmetrical encoder–decoder architecture. Central to our approach is the Convolutional Transposed Self-Attention Block (CTSAB), which is ingeniously conceived to capture long-range dependencies via channel-wise self-attention, while simultaneously encapsulating local context through depth-wise convolution. In addition, we refine the conventional feed-forward network by integrating Convolutional Gated Linear Units (CGLU) and deploy the Channel Coordinate Attention Block (CCAB) during the feature fusion phase to dynamically apportion weights across the feature map, thereby facilitating a more nuanced representation of pattern features endemic to infrared images. Through rigorous experimentation, we establish that our IDTransformer attains superior visual enhancement across five infrared image datasets, compared with the state-of-the-art methods. The source codes are available at https://github.com/szw811/IDTransformer .
Shen et al. (Thu,) studied this question.