Abstract
This study presents an improved non-local total variation (NLTV) model by using the block-matching and three-dimensional filtering (BM3D) algorithm for image denoising. First, the preprocessed image is obtained with the BM3D algorithm. Then, taking the place of the noisy image, the preprocessed image is used to construct the fidelity term of the energy functional and calculate the weight function in NLTV regularisation term. Finally, the energy functional is solved by the split Bregman algorithm. Experimental results demonstrate that the proposed model achieves better denoising performance than the original NLTV model in the visual appearance and objective indices, especially for the highly degenerated images. In addition, the proposed model can effectively suppress the appearance of the false information in the flat region, which overcomes the problem faced by the BM3D algorithm.
Highlights
In the field of image processing, image denoising is a very critical preprocessing step for the segmentation, reconstruction, recognition and so on
Zhang et al [5] proposed an adaptive total variation (TV) model based on a new indicator, that is, the local energy derived from the steerable filter; Yuan et al [6] proposed a regional spatially adaptive TV super-resolution algorithm, which performed the different regularisation strength with the difference curvature in the flat and structural regions of the image
We propose an improved non-local total variation (NLTV) model based on the BM3D algorithm for image denoising, in which the BM3D algorithm is used as a pre-filter to process the noisy image
Summary
In the field of image processing, image denoising is a very critical preprocessing step for the segmentation, reconstruction, recognition and so on. Based on the PDE theory, Rudin et al [4] proposed a classical total variation (TV) denoising model, which can effectively preserve image edges. The TV model causes staircase and over-smoothing effects in the denoised image. Zhang et al [5] proposed an adaptive TV model based on a new indicator, that is, the local energy derived from the steerable filter; Yuan et al [6] proposed a regional spatially adaptive TV super-resolution algorithm, which performed the different regularisation strength with the difference curvature in the flat and structural regions of the image. To overcome the oversmoothing effect, Liu et al [7] proposed a texture-preserved adaptive TV model based on the structure tensor. The TV regularisation, as a smoothing prior, is applied to remove multiplicative noise [8]
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