Image fusion-based contrast enhancement
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RESEARCH
Open Access
Image fusion-based contrast enhancement Amina Saleem1*, Azeddine Beghdadi1 and Boualem Boashash2,3
Abstract The goal of contrast enhancement is to improve visibility of image details without introducing unrealistic visual appearances and/or unwanted artefacts. While global contrast-enhancement techniques enhance the overall contrast, their dependences on the global content of the image limit their ability to enhance local details. They also result in significant change in image brightness and introduce saturation artefacts. Local enhancement methods, on the other hand, improve image details but can produce block discontinuities, noise amplification and unnatural image modifications. To remedy these shortcomings, this article presents a fusion-based contrastenhancement technique which integrates information to overcome the limitations of different contrastenhancement algorithms. The proposed method balances the requirement of local and global contrast enhancements and a faithful representation of the original image appearance, an objective that is difficult to achieve using traditional enhancement methods. Fusion is performed in a multi-resolution fashion using Laplacian pyramid decomposition to account for the multi-channel properties of the human visual system. For this purpose, metrics are defined for contrast, image brightness and saturation. The performance of the proposed method is evaluated using visual assessment and quantitative measures for contrast, luminance and saturation. The results show the efficiency of the method in enhancing details without affecting the colour balance or introducing saturation artefacts and illustrate the usefulness of fusion techniques for image enhancement applications. Keywords: contrast enhancement, image fusion, pyramidal image decomposition, Gaussian pyramid decomposition, image blending, luminance
1. Introduction The limitations in image acquisition and transmission systems can be remedied by image enhancement. Its principal objective is to improve the visual appearance of the image for improved visual interpretation or to provide better transform representations for subsequent image processing tasks (analysis, detection, segmentation, and recognition). Removing noise and blur, improving contrast to reveal details, coding artefact reduction and luminance adjustment are some examples of image enhancement operations. Achromatic contrast is a measure of relative variation of the luminance. It is highly correlated to the intensity gradient [1]. There is, however, no universal definition for the contrast. It is well established that human contrast sensitivity is a function of the spatial frequency; therefore, the spatial content of the image should be considered while defining the contrast. Based on this * Correspondence: [email protected] 1 L2TI-Institute Galilee, Universite Paris 13, Villetaneuse, France Full list of author information is available at the end of the article
property, the local band-limited contrast is defined by assigning a cont
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