Affiliations: [a] Graduate Institution of Automation Technology, National Taipei Technology University, Taipei, Taiwan, ROC | [b] Department Computer Science and Information Engineering, National Ilan University, Ilan, Taiwan, ROC
Abstract: Multiple exposure fusion (MEF) is attracting considerable attention in research on high dynamic range (HDR) imaging: Eliminating the need to generate an intermediate HDR image, MEF directly expands an image’s dynamic range and thus provides greater detail enhancement than traditional HDR techniques. However, in the fusion stage, the optimal weights of each pixel in the images input to the final synthesized image are challenging to determine and usually required manual tuning of parameters. In addition, many MEF algorithms have been proposed, but most have lacked a self-regulation mechanism. To tackle the above disadvantages, we apply fuzzy theory and present a novel MEF framework with a fuzzy feedback structure. In this work, over- and under-exposed images are generated from a single input image using local histogram stretching. This avoids the creation of ghost artifacts when multiple exposed images are fused in the dynamic scene containing object motion. In the fusion stage, fuzzy logic is used to determine pixel weights based on gradient and chrominance analysis, and a guided image filter is used to suppress noise and enhance edges in the weight maps. To ensure detail enhancement without excessive or insufficient sharpness, we developed a simple sharpness measure named the edge-map overlapping rate (EOR). With EOR and the feedback structure, users are allowed to manipulate the output synthesized image to their preferred sharpness level, and the above weights are appropriately redesigned by automatically regulating the magnitude of the fuzzy input. From experimental results, this work demonstrated excellent image quality and outperformed other existing HDR/MEF methods.
Keywords: Fuzzy logic, fuzzy feedback, high dynamic range (HDR), multiple exposure fusion (MEF)
