KEYWORDS: High dynamic range imaging, Cameras, Motion models, Optimization (mathematics), Visualization, Range imaging, Process modeling, Image processing, Control systems
High dynamic range (HDR) imaging expands the capabilities of a camera by synthesizing a sequence of different exposure images. However, due to camera and object motion, ghosts exist in the synthesized HDR image. The low-rank matrix completion (LRMC) model has achieved some success in ghost-free HDR imaging, but leads to artifacts around the observation region edges for neglecting local image structure. In this paper, a locality-constrained LRMC (LcLRMC) model is proposed, in which we iteratively update the background irradiance and the observation region based on the result from previous iteration. Specifically, the proposed method incorporates global and local structures. Experimental results show that compared to the conventional LRMC model, the proposed method effectively eliminates artifacts around the observation region edges.
In exposure fusion, it is challenging to remove artifacts because of camera motion and moving objects in the scene. An improved artifact removal method is proposed in this paper, which performs local linear adjustment in artifact removal progress. After determining a reference image, we first perform high-dynamic-range (HDR) deghosting to generate an intermediate image stack from the input image stack. Then, a linear Intensity Mapping Function (IMF) in each window is extracted based on the intensities of intermediate image and reference image, the intensity mean and variance of reference image. Finally, with the extracted local linear constraints, we reconstruct a target image stack, which can be directly used for fusing a single HDR-like image. Some experiments have been implemented and experimental results demonstrate that the proposed method is robust and effective in removing artifacts especially in the saturated regions of the reference image.
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