Enhancing Image Decomposition With Large Separable Kernel Attention in Generative Adversarial Networks

ABSTRACT The original generative adversarial network (GAN) model may struggle to adequately capture global information in images, particularly during complex decomposition tasks, leading to limitations in image clarity, detail retention and overall consistency. To address this challenge, we propose the large separable kernel attention generative adversarial network (LSKA‐GAN) model, building upon the blind image decomposition network (BIDeN). The LSKA module enhances BIDeN's global information capturing capability, thereby improving the quality and clarity of generated images. Experimental results demonstrate that LSKA‐GAN achieves obvious improvements in hybrid image decomposition. Compared to BIDeN, LSKA‐GAN exhibits an increase of 1.39 dB in peak signal‐to‐noise ratio (PSNR) and 0.04 in structural similarity index (SSIM). These improvements enable LSKA‐GAN to generate clearer images with more complete details, marking a notable advancement in image decomposition technology.