標題: Image Contrast Enhancement Using Classified Virtual Exposure Image Fusion
作者: Lee, Chang-Hsing
Chen, Ling-Hwei
Wang, Wei-Kang
資訊工程學系
Department of Computer Science
關鍵字: Classified virtual exposure image fusion;contrast enhancement;exposure fusion;image fusion
公開日期: 1-十一月-2012
摘要: In our daily life, digital cameras and smart phones have been widely used to take pictures. However, digital cameras and smart phones have a limited dynamic range, which is much lower than that human eyes can perceive. Thus, the photographs taken in high dynamic range scenes often exhibit under-exposure or over-exposure artifacts in shadow or highlight regions. In this study, an image fusion based approach, called classified virtual exposure image fusion (CVEIF), is proposed for image enhancement. First, a function imitating the F-stop concept in photography is designed to generate several virtual images having different intensity. Then, a classified image fusion method, which blends pixels in distinct luminance classes using different fusion functions, is proposed to produce a fused image in which every image region is well exposed. Experimental results on four different kinds of generic images, including a normal image, a low-contrast images, a backlight image, and a dark scene image, have shown that the proposed CVEIF approach produced more pleasingly enhanced images than other methods(1).
URI: http://dx.doi.org/10.1109/TCE.2012.6414993
http://hdl.handle.net/11536/21097
ISSN: 0098-3063
DOI: 10.1109/TCE.2012.6414993
期刊: IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Volume: 58
Issue: 4
起始頁: 1253
結束頁: 1261
顯示於類別:期刊論文


文件中的檔案:

  1. 000314168700021.pdf