Detail aware contrast enhancement with linear image fusion

A Comparative Study of Contrast Enhancement using Image Fusion

“…We do the experiment for the values of ɑ=0.5, ɑ=0.6 and ɑ=0.7, because of the better visual quality of the original image. According to [2] for ɑ=0.7 it gives better result in most of the cases.…”

“…We take the following expression [2] to obtain the fused image If of two images I1 and I2. If = ɑ*I1+ (1-ɑ)*I2 (3) where 0≤ ɑ≤1.…”

“…If we set the value of ɑ greater than 0.5 then the fused image will take more portion from the original image. According to [2] generally the visual quality of the original image is better than the equalized image and ɑ=0.7 works well so the experiment will be done for three consecutive values of ɑ that is 0.5, 0.6 and 0.7.…”

Image Enhancement and Image Hiding Based on Linear Image Fusion

“…Most of materials presented here have been published in (Hsieh et al, 2008;Hsieh et al, 2010;Kondo and Zhao, 2006). Apparently, image enhancement, image morphing, and image hiding are completely different technologies for different applications, they can actually be unified under the core of LIF, and this unification can be helpful in other related researches.…”

Image contrast enhancement using classified virtual exposure image fusion

“…Many software or image enhancement methods were developed to cope with these problems. In general, image enhancement methods can be classified into four categories: histogram-based methods [1]- [12], transformbased methods [1], [13], [14], exposure-based methods [15], [16], and image fusion based methods [17]- [19].…”