Image Contrast Enhancement Using Classified Virtual Exposure Image Fusion

Chang-Hsing Lee, Ling-Hwei Chen, Wei-Kang Wang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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).
Original languageEnglish
Pages (from-to)1253-1261
Number of pages9
JournalIEEE Transactions on Consumer Electronics
Issue number4
StatePublished - Sep 2012


  • Classified virtual exposure image fusion; contrast enhancement; exposure fusion; image fusion

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