Nighttime Image Stitching Method Based on Image Decomposition Enhancement

被引:0
作者
Yan, Mengying [1 ]
Qin, Danyang [1 ,2 ]
Zhang, Gengxin [1 ]
Tang, Huapeng [1 ]
Ma, Lin [3 ]
机构
[1] Heilongjiang Univ, Dept Elect Engn, Harbin 150080, Peoples R China
[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
[3] Harbin Inst Technol, Dept Elect & Informat Engn, Harbin 150080, Peoples R China
基金
中国国家自然科学基金;
关键词
night image stitching; image enhancement; feature extraction; edge enhancement; CONTRAST; ALGORITHM; EQUALIZATION; RETINEX;
D O I
10.3390/e25091282
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. 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.
引用
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页数:28
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