AWGN-Based Image Denoiser using Convolutional Vision Transformer
Alim Wicaksono Hari Prayuda, Heri Prasetyo, Jing-Ming Guo · 2021
This paper presents a new method for denoising an image corrupted with Additive White Gaussian Noise (AWGN). We adopt the effectiveness of Convolutional Vision Transformer (CvT) to suppress the occurred noise on an image. The proposed method exploits the residual learning approach in order to estimate and reduce the noise on a noisy image. Herein, the model is trained in the end-to-end manner to capture the relation between the noisy image and its noise map. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.