GIADNet: Gradient Inspired Attention Driven Denoising Network

Image noise, commonly introduced during the acquisition process, significantly degrades visual quality and adversely affects downstream image processing tasks. To address this challenge while preserving fine structural details, we propose GIADNet: a Gradient-Inspired Attention-Driven Denoising Network. The proposed framework integrates gradient-guided feature enhancement, multi-scale representation learning, and attention-based refinement to achieve a superior balance between noise suppression and detail retention. In particular, the gradient information of the noisy input is fused with deep features early in the pipeline to enrich semantic representation. Furthermore, we introduce two dedicated modules: the Multi-Pooling Pixel Attention (MPPA) module, which adaptively emphasizes informative pixels, and the Multi-Scale Attention Block (MSAB), designed to capture hierarchical contextual dependencies across varying spatial resolutions. Extensive experiments on standard benchmarks demonstrate that GIADNet achieves highly competitive performance, surpassing several state-of-the-art methods in both quantitative metrics and visual quality. Ablation studies further validate the effectiveness of each component, underscoring the importance of our attention-guided multi-scale design in advancing the field of image denoising. Code is available at: https://github.com/debashis15/GIADNet.