Image Denoising: Invertible and General Denoising Frameworks
type: CreativeWork
description: The widespread use of digital cameras has resulted in a massive number of images being taken every day. However, due to the limitations of sensors and environments such as light conditions, the images are usually contaminated by noise. Obtaining visually clean images are essential for the accuracy of downstream high-level vision tasks. Thus, denoising is a crucial preprocessing step. A fundamental challenge in image denoising is to restore recognizable frequencies in edge and fine-scaled texture regions. Traditional methods usually employ hand-crafted priors to enhance the restoration of these high frequency regions, which seem to be omitted in current deep learning models. We explored whether the clean gradients can be utilized in deep networks as a prior as well as how to incorporate this prior in the networks to boost recovery of missing or obscured picture elements. We present results showing that fusing the pre-denoised images' gradient in the shallow layer contributes to recovering better edges and textures. We also propose a regularization loss term to ensure that the reconstructed images' gradients are close to the clean gradients. Both techniques are indispensable for enhancing the restored image frequencies. We also studied how to make the network preserve input information for better restoration of the high-frequency details. According to the definition of mutual information, we presented that invertibility is indispensable for information losslessness. Then, we proposed the Invertible Restoring Autoencoder (IRAE) network, a multiscale invertible encoder-decoder network. The superiority of this network was verified on three different low-level tasks, image denoising, JPEG image decompression and image inpainting. IRAE showed a good direction to explore more invertible architectures for image restoration. We attempted to further reduce the model size of invertible restoration networks. Our intuition was to use the same learned parameters to encode the nois…
abstract: The widespread use of digital cameras has resulted in a massive number of images being taken every day. However, due to the limitations of sensors and environments such as light conditions, the images are usually contaminated by noise. Obtaining visually clean images are essential for the accuracy of downstream high-level vision tasks. Thus, denoising is a crucial preprocessing step. A fundamental challenge in image denoising is to restore recognizable frequencies in edge and fine-scaled texture regions. Traditional methods usually employ hand-crafted priors to enhance the restoration of these high frequency regions, which seem to be omitted in current deep learning models. We explored whether the clean gradients can be utilized in deep networks as a prior as well as how to incorporate this prior in the networks to boost recovery of missing or obscured picture elements. We present results showing that fusing the pre-denoised images' gradient in the shallow layer contributes to recovering better edges and textures. We also propose a regularization loss term to ensure that the reconstructed images' gradients are close to the clean gradients. Both techniques are indispensable for enhancing the restored image frequencies. We also studied how to make the network preserve input information for better restoration of the high-frequency details. According to the definition of mutual information, we presented that invertibility is indispensable for information losslessness. Then, we proposed the Invertible Restoring Autoencoder (IRAE) network, a multiscale invertible encoder-decoder network. The superiority of this network was verified on three different low-level tasks, image denoising, JPEG image decompression and image inpainting. IRAE showed a good direction to explore more invertible architectures for image restoration. We attempted to further reduce the model size of invertible restoration networks. Our intuition was to use the same learned parameters to encode the nois…
author: Liu, Yang
language: en_AU
date published: 2022
date modified: 2026-07-24T01:07:16Z
provider: Australian Catholic University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian Catholic University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1885/270008