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The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.<\/jats:p>","DOI":"10.3390\/e25091282","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T09:14:41Z","timestamp":1693473281000},"page":"1282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Nighttime Image Stitching Method Based on Image Decomposition Enhancement"],"prefix":"10.3390","volume":"25","author":[{"given":"Mengying","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Heilongjiang University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-6121","authenticated-orcid":false,"given":"Danyang","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Heilongjiang University, Harbin 150080, China"},{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gengxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Heilongjiang University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huapeng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Heilongjiang University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1684-9714","authenticated-orcid":false,"given":"Lin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103972","DOI":"10.1016\/j.imavis.2020.103972","article-title":"Constructing big panorama from video sequence based on deep local feature","volume":"101","author":"Cao","year":"2020","journal-title":"Image Vis. 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