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Blind Image Deblurring Using Row-Column Sparse Representations
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DOI：10.1109/LSP.2017.2782570.png)
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Abstract
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Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recover the deblurred image. The problem is of strong practical relevance sincemany imaging devices, such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality. Despite significant research effort, handling largemotions remains an open problem. In this letter, we develop a newmethod called blind image deblurring using row-column sparsity (BD-RCS) to address this issue. Specifically, wemodel the outer product of kernel and image coefficients in certain transformation domains as a rank-one matrix, and recover it by solving a rankminimization problem. Our central contribution then includes solving two new optimization problems involving RCS to automatically determine blur kernel and image support sequentially. The kernel and image can then be recovered through a singular value decomposition. Experimental results on linear motion deblurring demonstrate that BD-RCS can yield better results than state of the art, particularly when the blur is caused by large motion. This is confirmed both visually and through quantitative measures.
Keywords:
Blind image deblurring
motion blur kernel
rank minimization
row and column sparsity
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