M2ATNet: Multi-Scale Multi-Attention Denoising and Feature Fusion Transformer for Low-Light Image Enhancement

Images taken in dim environments frequently exhibit issues like insufficient brightness, noise, color shifts, and loss of detail. These problems pose significant challenges to dark image enhancement tasks. Current approaches, while effective in global illumination modeling, often struggle to simultaneously suppress noise and preserve structural details, especially under heterogeneous lighting. Furthermore, misalignment between luminance and color channels introduces additional challenges to accurate enhancement. In response to the aforementioned difficulties, we introduce a single-stage framework, M2ATNet, using the multi-scale multi-attention and Transformer architecture. First, to address the problems of texture blurring and residual noise, we design a multi-scale multi-attention denoising module (MMAD), which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities. Secondly, to solve the non-alignment problem of the luminance and color channels, we introduce the multi-channel feature fusion Transformer (CFFT) module, which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction. To guide the model to learn more stably and efficiently, we also fuse multiple types of loss functions to form a hybrid loss term. We extensively evaluate the proposed method on various standard datasets, including LOL-v1, LOL-v2, DICM, LIME, and NPE. Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches. Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.