  Removing noise from images is a challenging and fundamental problem in the
field of computer vision. Images captured by modern cameras are inevitably
degraded by noise which limits the accuracy of any quantitative measurements on
those images. In this project, we propose a novel image reconstruction
framework which can be used for tasks such as image denoising, deblurring or
inpainting. The model proposed in this project is based on Vision Transformer
(ViT) that takes 2D images as input and outputs embeddings which can be used
for reconstructing denoised images. We incorporate four additional optimization
techniques in the framework to improve the model reconstruction capability,
namely Locality Sensitive Attention (LSA), Shifted Patch Tokenization (SPT),
Rotary Position Embeddings (RoPE) and adversarial loss function inspired from
Generative Adversarial Networks (GANs). LSA, SPT and RoPE enable the
transformer to learn from the dataset more efficiently, while the adversarial
loss function enhances the resolution of the reconstructed images. Based on our
experiments, the proposed architecture outperforms the benchmark U-Net model by
more than 3.5\% structural similarity (SSIM) for the reconstruction tasks of
image denoising and inpainting. The proposed enhancements further show an
improvement of \textasciitilde5\% SSIM over the benchmark for both tasks.
