Saliency guided naturalness enhancement in color images

Abstract

Recent technological advances have enabled smartphones, tablet computers and wearable devices to acquire digital images conveniently. Thus, taking pictures, enjoying them, and using them in a variety of applications (e.g., social networking) have become an important part of technology based lifestyle for many people. As a result, efficient algorithms for image enhancement play an increasing role for improving image quality for both human visualization and computer image analysis. In this paper, we learn from the human visual system (HVS) which has a powerful biological mechanism to optimize scene perception in complex and varying illumination conditions. Using this knowledge, we combine the concept of popular Retinex and the histogram equalization (HE) and propose an efficient image naturalness enhancement algorithm for both non-uniform and low light images. Our algorithm emphasizes the perceptual contrast while reducing halo artifacts, detail-clipping effects, and over-enhancements. An input lightness is computed based on the white-patch assumption. A Retinex based lightness correction is utilized to preserve and improve the contrast for bright regions. The halo artifacts are suppressed based on Bilateral Filtering (BF). The detail-clipping effect is reduced by imposing certain constraints. For dark regions in input images, a perceptual contrast enhancement method with dynamic range adjustment is adopted which enhances perceptual contrast and adjusts dynamic range without over-enhancements. Finally, a weighting combination guided by a simplified saliency map is employed to blend results of enhancements. Our method produces more natural image appearance and better perceptual contrast in comparison with a number of state-of-the-art algorithms. Besides, the proposed algorithm is relatively simple and efficient in computation, suitable for implementation in mobile and wearable devices to support a variety of practical applications.