For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.
This paper proposes an Image Contrast Enhancement (ICE) method based on using an Improved Chicken Swarm Optimization (ICSO) algorithm to enhance images while at the same time preventing over-enhancement. In the optimization process, a new practical objective function is employed to reach three main goals, preserving the main details, generating an image with a uniform histogram, and reducing the spikes in the modified histogram. In the proposed approach, the RGB color channels are optimized individually. The performance of the proposed method is suitable for enhancing the contrast of low-and high-contrast images. A subjective experiment is designed to visually evaluate and compare the results with other ICE methods. The simulation results on the CSIQ, TID2013, and SEID datasets show that the proposed method outperforms numerous traditional and state-of-the-art ICE techniques both subjectively and objectively. The most important advantage of the newly proposed technique is that there is an agreement among observers on when over-enhancement occurs regardless of whether the Initial processed image was of low or high contrast. INDEX TERMSContrast enhancement, image quality assessment, over-enhancement.
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