Self2Align: a self-supervised denoising framework for single-scene images

Jian Wang

doi:10.1117/12.2659707

22 November 2022 Self2Align: a self-supervised denoising framework for single-scene images

Jian Wang

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 1247509 (2022) https://doi.org/10.1117/12.2659707
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

In recent years, driven by the development of deep neural networks, image denoising technology has made great progress. One of the most representative technologies is the deep neural network denoiser based on supervised learning. The denoiser uses noise-clean image pairs as the input and output of a deep neural network, and trains the deep neural network to achieve the denoising goal. However, collecting many high-quality noise-clean image pairs is extremely challenging, which is mainly because (1) it is difficult to collect true and clean images; and (2) changes in motion and lighting make it impossible to align collected image pairs, which limits the widespread application of supervised learning denoising techniques. To solve the above problems, this paper proposes a simple and effective method Self2Align to train a deep neural network denoiser. First, we proposed an efficient deep network model for inter-image alignment. For the collection of original images, we only use noisy images and collected multiple images for different scenes. The trained alignment network was then used to align the original image pairs automatically. Second, the aligned image pairs generated in the first stage were used as training image pairs for the training of the denoising network. In addition, we introduced a new training strategy so that the network can obtain better performance. The proposed Self2Align architecture eliminates the reliance on noise-clean image pairs and reduces the acquisition difficulty of training image pairs in terms of self-supervised training of the network. We explained the feasibility of our proposed method through theoretical analysis and obtained competitive results through experimental verification.

Citation Download Citation

Jian Wang "Self2Align: a self-supervised denoising framework for single-scene images", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 1247509 (22 November 2022); https://doi.org/10.1117/12.2659707

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Denoising

Image processing

Image denoising

Machine learning

Network architectures

Neural networks

Computer programming

Show All Keywords

Keywords/Phrases

Search In:

Publication Years