Image denoising using deep learning: Comparative study - Eleyan, Gülden (Edelweiss Applied Science and Technology, 2025)
Deep learning offers a flexible and effective approach to automated image denoising. This study investigated the residual learning capabilities of two recent deep learning networks: the Restoration Transformer (Restormer) network and the Deep CNN (DnCNN) network. We compared their denoising performance on the BSD68 dataset under varying levels of Gaussian noise against established algorithms such as block-matching and 3D filtering (BM3D) and trainable nonlinear reaction diffusion (TNRD). Our findings demonstrate that the Restormer algorithm excels in noise removal. This highlights the potential of transformer-based architectures in image restoration tasks, surpassing traditional methods in achieving superior denoising quality. Further research can explore the application of Restormer to other noise types and datasets.