Depth estimation for hand-held light field cameras under low light conditions
IC3D(2014)
摘要
Depth estimation is one of the new functions provided by hand-held light field cameras. However, the quality of depth estimation is very sensitive to noise, which is especially a problem for scenes under low light conditions. In this paper, we propose a depth estimation flow for light field data, which can be fully-automated and no noise characteristics are required a priori. The results of Root Mean Square Error (RMSE) and Percentage of Bad Matching Pixels (PBM) show the effectiveness of this iterative correlation-based depth estimation flow even with basic filtering functions.
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关键词
iterative correlation-based depth estimation flow,rmse,depth estimation,low light conditions,filtering functions,light field,image denoising,pbm,least mean squares methods,cameras,denoising,percentage of bad matching pixels,noise-resilient depth estimation flow,filtering theory,root mean square error,denoising processes,iterative methods,hand-held light field cameras,noise,noise reduction,estimation,correlation
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