Local brightness adaptive image colour enhancement with Wasserstein distance. Issue 1 (1st January 2015)

• Record Type: Journal Article
• Authors: Wang, Liqian; Xiao, Liang; Liu, Hongyi; Wei, Zhihui
• Is Part Of: IET image processing. Volume 9:Issue 1(2015)
• Page Start: 43
• Page End: 53
• Publication Date: 2015-01-01
• Subjects: image colour analysis -- image enhancement -- image segmentation -- gradient methods; local brightness adaptive image colour enhancement -- Wasserstein distance -- image analysis -- image segmentation -- pattern recognition -- energy functional -- contrast energy -- Wasserstein dispersion energy -- image dynamic range -- image local patch -- gradient descent algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0209
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Colour image enhancement is an important preprocessing phase of many image analysis tasks such as image segmentation, pattern recognition and so on. This study presents a new local brightness adaptive variational model using Wasserstein distance for colour image enhancement. Under the perceptually inspired variational framework, the proposed energy functional consists of an improved contrast energy term and a Wasserstein dispersion energy term. To better adjust image dynamic range, the authors propose a local brightness adaptive contrast energy term using the average brightness of image local patch as the local brightness indicator. To restore image true colours, a Wasserstein distance‐based dispersion energy term is used to measure the statistical similarity between the original image and the enhanced image. The proposed energy functional is minimised by using a gradient descent algorithm. Two objective measures are used to quantitatively measure the enhancement quality. Experimental results demonstrate the efficiency of the proposed model for removing colour cast and haze, enhancing contrast, recovering details and equalising low key images.