Image contrast enhancement using classified virtual exposure image fusion

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.