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Multiple feature-based contrast enhancement of ROI of backlit images

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Abstract

Backlit image is obtained when image is captured with intense reflection of light. It is a frequently observed condition of lighting that can cause significant image quality deterioration. They are a combination of dark and bright regions, and the objects in the image generally appear to be dark. The region of interest (ROI) depicts some dark image regions or objects present in the image. Such ROI has low contrast in backlit images; therefore, visualization is uncertain. In order to visualize the contents properly, enhancement of ROI in backlit images is essential. A novel and simplified approach based on the multiple features of ROI of backlit images is proposed. The proposed approach’s fundamental idea is to blend different features into a single one to enhance the ROI. Global tone mappings, namely gamma correction and logarithmic transform, are performed while preserving global and local contrast effectively to improve the visual quality. In the next step, gradient map and filter-based operations were performed to preserve the image’s naturalness. Furthermore, the proposed method introduces weight maps based on the exposedness to increase the visibility and the fusion of the results. Experimental results based on the contrast measure (CM), discrete entropy (DE), and balanced mean magnitude of relative error (BMMRE) of discrete entropy reveal the proposed approach’s effectiveness and its gains in visual consistency over existing backlit image enhancement algorithms.

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  1. Department of Computer Applications, National Institute of Technology, Jamshedpur, Jharkhand, 831014, India

    Gaurav Yadav & Dilip Kumar Yadav

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  1. Gaurav Yadav
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  2. Dilip Kumar Yadav
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Correspondence to Dilip Kumar Yadav.

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Yadav, G., Yadav, D.K. Multiple feature-based contrast enhancement of ROI of backlit images. Machine Vision and Applications 33, 14 (2022). https://doi.org/10.1007/s00138-021-01272-9

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