Image Denoising via Deep Learning Neural Networks and Transformers

This thesis investigates the implementation of image denoising using Neural Networks (NN) in conjunction with wavelet transform techniques. For the purpose of data simulation, images are intentionally corrupted with additive white Gaussian noise (AWGN) at various noise levels. Additionally, the study includes the examination of both random noise and real-world noise scenarios. This research has an emphasis on implementing several novel neural network architectures and strategies. By integrating the wavelet transform, the research aims to leverage its multi-resolution analysis capability to further enhance denoising performance. The proposed methods underscore the ability of residual learning and Transformers to integrate both local and global features, which efficiently capture fine-grained local details and long-range global dependencies. Keywords: image denoising, deep learning, residual learning, Transformers.