https://scholars.lib.ntu.edu.tw/handle/123456789/581159
標題: | JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal | 作者: | Chen W.-T Fang H.-Y Ding J.-J Tsai C.-C Kuo S.-Y. SY-YEN KUO |
關鍵字: | Computer vision; Convolution; Image enhancement; Object detection; Street cleaning; Transparency; Atmospheric phenomena; Real-world datasets; Single images; Snow models; Snow particles; Snow removal; Snow | 公開日期: | 2020 | 卷: | 12366 LNCS | 起(迄)頁: | 754-770 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Snow removal usually affects the performance of computer vision. Comparing with other atmospheric phenomenon (e.g., haze and rain), snow is more complicated due to its transparency, various size, and accumulation of veiling effect, which make single image de-snowing more challenging. In this paper, first, we reformulate the snow model. Different from that in the previous works, in the proposed snow model, the veiling effect is included. Second, a novel joint size and transparency-aware snow removal algorithm called JSTASR is proposed. It can classify snow particles according to their sizes and conduct snow removal in different scales. Moreover, to remove the snow with different transparency, the transparency-aware snow removal is developed. It can address both transparent and non-transparent snow particles by applying the modified partial convolution. Experiments show that the proposed method achieves significant improvement on both synthetic and real-world datasets and is very helpful for object detection on snow images. ? 2020, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097370878&doi=10.1007%2f978-3-030-58589-1_45&partnerID=40&md5=137d806a6a1632aa64c68ab7b371aaaf https://scholars.lib.ntu.edu.tw/handle/123456789/581159 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-58589-1_45 |
顯示於: | 電機工程學系 |
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