Enhancing image visuality by multi-exposure fusion

Abstract Image visuality enhancement aims at increasing visual quality of a given image to convey more useful information. The key for visuality enhancement is to comprehensively exploit the details of the image scene. However, one (or several) observed image only provides partial information of the scene. To address this problem, we present a novel multi-exposure fusion based visuality enhancement method in this study. Firstly, we propose a simulated exposure model to synthesize the results of the observed image under different exposure conditions. Then, white balancing and image gradient are separately performed on those simulated exposure results. By doing these, scene details in different exposure conditions as well as image feature spaces (i.e., color and gradient spaces) can be well exploited. To appropriately take advantage of those resultant details, we adopt the Laplacian pyramid based linear fusion framework to integrate them into the enhanced image in a multi-scale way. Different from conventional fusion methods, we develop a more powerful weight map for fusion, which is able to simultaneously highlight pixels with good exposedness, contrast, saturation as well as Gamut. Experimental results demonstrate that the proposed method can effectively enhance the visuality of the observed image. Compared with existing methods, the proposed method well highlights the fine details as well as avoiding halo artifacts.