{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T13:13:03Z","timestamp":1784898783423,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.<\/jats:p>","DOI":"10.3390\/s22155640","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"5640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Binarization Algorithm Based on Side Window Multidimensional Convolution Classification"],"prefix":"10.3390","volume":"22","author":[{"given":"Hong","family":"Ren","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1117\/12.621275","article-title":"Analysis and design of solid corner cube reflectors for a space navigation application","volume":"5798","author":"Granade","year":"2005","journal-title":"Proc. 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