A Transformer Network Combing CBAM for Low-Light Image Enhancement

Recently, a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement, yielding remarkable outcomes. Due to the intricate nature of imaging scenarios, including fluctuating noise levels and unpredictable environmental elements, these techniques do not fully resolve these challenges. We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture, merging the Convolutional Block Attention Module (CBAM) with the Transformer. Our model is capable of detecting more prominent features across both channel and spatial domains. We have conducted extensive experiments across several datasets, namely LOLv1, LOLv2-real, and LOLv2-sync. The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Moreover, we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison, and the experimental data clearly demonstrate that our approach excels visually over other methods as well.