A two-stream deep neural network for infrared image enhancement

Zhongxiang Pang; Guihua Liu; Guosheng Li; Jian Gong; Chunmei Chen; Chao Yao

doi:10.1117/12.2642633

20 January 2023 A two-stream deep neural network for infrared image enhancement

Zhongxiang Pang, Guihua Liu, Guosheng Li, Jian Gong, Chunmei Chen, Chao Yao

Proceedings Volume 12563, AOPC 2022: AI in Optics and Photonics; 1256302 (2023) https://doi.org/10.1117/12.2642633
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

Aiming at the problems of low contrast and blurry details of infrared images, a novel two-stream deep full convolutional neural network is proposed for low-quality infrared image enhancement. An infrared detail enhancement sub-network and a global content-invariant sub-network are designed to achieve adaptive enhancement of infrared features. First, the detail enhancement network composed of mixed attention block with multi-convolutions, residual learning and up-sampling unit is used to extract deep features from inputs, learn meaningful thermal radiation target information, restrain unnecessary background, and then perform the separation of target and background. Second, the content-invariant network mainly consisting of dilated convolutions and multi-scale convolutions captures rich contextual information to focus on the overall content, maintain the spatial structure, and avoid over-enhancement of local regions. Finally, the fine-tuning unit fuses the features extracted by the two-stream to complete the element complementation between different mappings and generate high-quality infrared images. Furthermore, experiments on public datasets and self-collected infrared datasets demonstrate that the proposed method outperforms other image enhancement methods not only for image quality on PSNR and SSIM, but also that has better visual quality with less artifact and noise.

Citation Download Citation

Zhongxiang Pang, Guihua Liu, Guosheng Li, Jian Gong, Chunmei Chen, and Chao Yao "A two-stream deep neural network for infrared image enhancement", Proc. SPIE 12563, AOPC 2022: AI in Optics and Photonics, 1256302 (20 January 2023); https://doi.org/10.1117/12.2642633

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Infrared imaging

Image enhancement

Thermography

Convolution

Feature extraction

Infrared detectors

Image quality

Show All Keywords

Keywords/Phrases

Search In:

Publication Years