Blind Super-Resolution Network with Dual-Channel Attention for Images Captured by Sub-Millimeter-Diameter Fiberscope

Author:

Chen Wei1,Liu Yi1,Zhang Jie1,Duan Zhigang1,Zhang Le1,Hou Xiaojuan1,He Wenjun1,You Yajun2ORCID,He Jian1,Chou Xiujian1

Affiliation:

1. Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China

2. School of Aerospace Engineering, North University of China, Taiyuan 030051, China

Abstract

A blind super-resolution network with dual-channel attention is proposed for images captured by the 0.37 mm diameter sub-millimeter fiberscope. The fiberscope can used in scenarios where other image acquisition devices cannot be applied based on its flexible, soft, and minimally invasive characteristics. However, the images have black reticulated noise and only 3000 pixels. To improve image quality, the Butterworth band-stop filter is used to reduce the frequency of the reticulated noise. By optimizing the blind super-resolution model, high-quality images can be reconstructed that do not require a lot of synthetic paired fiberscope image data. Perceptual loss is utilized as a loss function, and channel and spatial attention mechanisms are introduced to the model to enhance the high-frequency detail information of the reconstructed image. In the comparative experiment with other methods, our method showed improvements of 2.25 in peak signal-to-noise ratio (PSNR) and 0.09 in structural similarity (SSIM) based on objective evaluation metrics. The learned perceptual image patch similarity (LPIPS) based on learning was reduced by 0.6. Furthermore, four different methods were used to enhance the resolution of the fiberscope images by a factor of four. The results of this paper improve the information entropy and Laplace clarity by 0.44 and 2.54, respectively, compared to the average of other methods. Validation results show that the approach in this paper is more applicable to sub-millimeter-diameter fiberscopes.

Funder

National Natural Science Foundation of China

Fundamental Research Program of Shanxi Province

Program for the Innovative Talents of Higher Education Institutions of Shanxi, the Central Guidance on Local Science and Technology, Development Fund of Shanxi Province

Foundation of Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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