Self-Supervised Denoising Transformer With Gaussian Process

Rajeev Yasarla, Jeya Maria Jose Valanarasu, Vishwanath Sindagi, Vishal M. Patel; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 1474-1484

Convolutional neural network (CNN) based methods have been the main focus of recent developments for image denoising. However, these methods lack majorly in two ways: 1) They require a large amount of labeled data to perform well. 2) They do not have a good global understanding due to convolutional inductive biases. Recent emergence of Transformers and self-supervised learning methods have focused on tackling these issues. In this work, we address both these issues for image denoising and propose a new method: Self-Supervised denoising Transformer (SST-GP) with Gaussian Process. Our novelties are two fold: First, we propose a new way of doing self-supervision by incorporating Gaussian Processes (GP). Given a noisy image, we generate multiple noisy down-sampled images with random cyclic shifts. Using GP, we formulate a joint Gaussian distribution between these down-sampled images and learn the relation between their corresponding denoising function mappings to predict the pseudo-Ground truth (pseudo-GT) for each of the down-sampled images. This enables the network to learn noise present in the down-sampled images and achieve better denoising performance by using the joint relationship between down-sampled images with help of GP. Second, we propose a new transformer architecture - Denoising Transformer (Den-T) which is tailor-made for denoising application. Den-T has two transformer encoder branches - one which focuses on extracting fine context details and another to extract coarse context details. This helps Den-T to attend to both local and global information to effectively denoise the image. Finally, we train Den-T using the proposed self-supervised strategy using GP and achieve a better performance over recent unsupervised/self-supervised denoising approaches when validated on various denoising datasets like Kodak, BSD, Set-14 and SIDD. Codes will be made public after review.