Swin transformer and fusion for underwater image enhancement

Jinghao Sun; Junyu Dong; Qingxuan Lv

doi:10.1117/12.2626075

30 April 2022 Swin transformer and fusion for underwater image enhancement

Jinghao Sun, Junyu Dong, Qingxuan Lv

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217734 (2022) https://doi.org/10.1117/12.2626075
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

In an underwater scene, refraction, absorption, and scattering of light by suspended particles in water degrade the visibility of images, causing low contrast, blurred details and color distortion. Based on the characteristics of underwater image degradation, we proposed a fusion neural network, which builds on the blending of two images that are derived from a white-balanced and color-compensated version of the raw underwater image. The two images are fused through the image enhancement module. In the previous works, convolutional neural networks (CNN) have been widely used in underwater image enhancement tasks. However, the local computational characteristics of convolutional operations limit the effect of the image enhancement. Recently, transformers have shown impressive performance on low-level vision tasks. In this paper, we propose a module SwinMT for image enhancement based on the swin transformer. First, we generate two inputs by respectively applying white balance (WB) and gamma correction (GC) algorithms to an underwater image. Second, the SwinMT module extracts features respectively, which consists of two parts: low-frequency feature extraction module and high-frequency feature to restore high-quality. We conduct experiments on rendered synthetic underwater images. Experiments on underwater images show that our method produces visually pleasing results, and we compare results with state-of-the-art techniques.

Citation Download Citation

Jinghao Sun, Junyu Dong, and Qingxuan Lv "Swin transformer and fusion for underwater image enhancement", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217734 (30 April 2022); https://doi.org/10.1117/12.2626075

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
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image enhancement

Image fusion

Transformers

Feature extraction

Light scattering

Data modeling

Scattering

Show All Keywords

Keywords/Phrases

Search In:

Publication Years