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 contrast-enhancement technique which integrates information to overcome the limitations of different contrast-enhancement 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.
Highlights
The limitations in image acquisition and transmission systems can be remedied by image enhancement
We select the MR scheme proposed in [37] as we want to guide the fusion of contrast-enhanced images by weighing them according to a weight map
The first part briefly describes the metrics to assess the performance of contrast-enhancement methods
Summary
The limitations in image acquisition and transmission systems can be remedied by image enhancement. For the proposed fusion application, the less contrasted and saturated regions should receive less weight (less salience), while interesting areas containing bright colours and details (high visual saliency) should have high weight Based on this requirement, weights (for the fusion process) are computed by combining the measures defined (according to the visual saliency) for the contrast, saturation and luminance. The fusion method introduced in [37] is inspired by the pyramidal decomposition scheme proposed in [49] and the blending introduced in [48] It blends the pyramid coefficients based on a scalar weight map. We select the MR scheme proposed in [37] as we want to guide the fusion of contrast-enhanced images by weighing them according to a weight map (computed from quality metrics defined for luminance, saturation and contrast of the enhanced images).
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