Abstract
Low-rank (LR) representation and the nonlocal model (NLM) are important techniques in the field of image restoration, offering significant improvements over many current recovery algorithms. Natural images contain global and local redundancy, and this can be utilized to enhance the restoration performance. Thus, we propose a novel optimization framework that incorporates the benefits of LR and NLM. First, NLM is employed to search for similar patches to reduce the global redundancy. An LR model is then exploited as the prior knowledge needed to constrain the low-rank property of the searched patches. We also use a 3D sparse model to constrain the local sparsity of these patches, thus preserving their underlying structure more effectively. To solve the minimization problem within our novel framework, we describe an iterative scenario that uses an alternating optimization method based on the improved split Bregman technique. Experimental results demonstrate that our proposed method outperforms several state-of-the-art algorithms.
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This work was supported by National Natural Science Foundation of China (Grant No. 61501147) and Natural Science Foundation of Hei Longjiang China (Grant No. F2015040).
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Li, A., Chen, D., Lin, K. et al. Nonlocal Joint Regularizations Framework with Application to Image Denoising. Circuits Syst Signal Process 35, 2932–2942 (2016). https://doi.org/10.1007/s00034-015-0179-1
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DOI: https://doi.org/10.1007/s00034-015-0179-1