DADNet: Dual-Branch Low-Light Image Enhancement Network Based on Attention Mechanism and Dark Channel Prior

Images captured in low-light conditions often have poor visibility, low contrast, and color distortion due to uneven lighting. Most existing enhancement methods often suffer from unstable brightness recovery and color cast, which affect both visual quality and performance of advanced vision tasks. To address those issues, we propose DADNet, a dual-branch network with an attention mechanism and dark channel prior containing an Illumination Enhancement Module (IEM) and Color Transformation Module (CTM). The IEM extracts multi-scale features and improves lighting based on the dark channel prior, while the CTM employs the attention mechanism to handle color features and adjust saturation adaptively. Experimental results on three datasets show that DADNet performs well in both qualitative and quantitative evaluations. It effectively preserves image structure and texture details while achieving a good balance between overall brightness and color quality.