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基于反射式微扫描光学系统的自监督超分辨率恢复算法研究

Research on self-supervised super resolution restoration algorithm based on reflective micro-scanning optical system.

作者信息

Chen Jian, Wang Yuwei, Ye Xin, Chen Mo, Zhou Qun

机构信息

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.

University of Chinese Academy of Sciences, Beijing, 100039, China.

出版信息

Sci Rep. 2025 Jul 9;15(1):24736. doi: 10.1038/s41598-025-09834-x.

DOI:10.1038/s41598-025-09834-x
PMID:40634544
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12241496/
Abstract

The features of infrared image such as less details and low SNR become bottleneck of infrared image application. This paper mainly focuses on research of super-resolution restoration algorithm of infrared image based on reflective infrared micro-scanning optical system. Aiming at solving super-resolution restoration problem of infrared image, self-supervised super resolution restoration algorithm is proposed and optimized. Meanwhile, reflective infrared micro-scanning optical system is introduced to break theoretical limit of simple image processing algorithm. And performance of infrared image super-resolution restoration is improved.

摘要

红外图像细节少、信噪比低等特点成为红外图像应用的瓶颈。本文主要围绕基于反射式红外微扫描光学系统的红外图像超分辨率复原算法展开研究。针对红外图像超分辨率复原问题，提出并优化了自监督超分辨率复原算法。同时，引入反射式红外微扫描光学系统以突破简单图像处理算法的理论极限，进而提升红外图像超分辨率复原的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/07331b167900/41598_2025_9834_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/faba760080f2/41598_2025_9834_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/22766fdc6e00/41598_2025_9834_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/c4684550cf4e/41598_2025_9834_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/d9596f670c09/41598_2025_9834_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/e15e13737710/41598_2025_9834_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/31a2aa06849a/41598_2025_9834_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/07331b167900/41598_2025_9834_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/faba760080f2/41598_2025_9834_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/22766fdc6e00/41598_2025_9834_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/c4684550cf4e/41598_2025_9834_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/d9596f670c09/41598_2025_9834_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/e15e13737710/41598_2025_9834_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/90332d9f9ebf/41598_2025_9834_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/e513ffc955f9/41598_2025_9834_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/f540c12fce74/41598_2025_9834_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/3083b58fba1b/41598_2025_9834_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/c99fc5116454/41598_2025_9834_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/60c18167da85/41598_2025_9834_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/4f0419d2f480/41598_2025_9834_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/2f0bb9b7e4cb/41598_2025_9834_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/9c57889e09ab/41598_2025_9834_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/31a2aa06849a/41598_2025_9834_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/114d/12241496/07331b167900/41598_2025_9834_Fig16_HTML.jpg

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Research on region selection super resolution restoration algorithm based on infrared micro-scanning optical imaging model.
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A POCS super resolution restoration algorithm based on BM3D.一种基于三维块匹配滤波（BM3D）的现场快速检测（POCS）超分辨率恢复算法。
Sci Rep. 2017 Nov 8;7(1):15049. doi: 10.1038/s41598-017-15273-0.