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An effective nonlocal means image denoising framework based on non-subsampled shearlet transform

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Abstract

Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artefact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. Initially, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition levels of NSST is set to two, resulting in set of low-frequency coefficients (coarser layer) and four sets high-frequency coefficients (finer layers). The two number of levels of decomposition are used in order to preserve memory, reduce processing time, and mitigate the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter preserves textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.

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Authors and Affiliations

  1. Department of ECE, Chandigarh University, Mohali, India

    Bhawna Goyal

  2. Ronin Institute, Montclair, NJ, 07043, USA

    Ayush Dogra

  3. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India

    Arun Kumar Sangaiah

  4. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu, Taiwan

    Arun Kumar Sangaiah

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  1. Bhawna Goyal
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  2. Ayush Dogra
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Correspondence to Arun Kumar Sangaiah.

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Communicated by Irfan Uddin.

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Goyal, B., Dogra, A. & Sangaiah, A.K. An effective nonlocal means image denoising framework based on non-subsampled shearlet transform. Soft Comput 26, 7893–7915 (2022). https://doi.org/10.1007/s00500-022-06845-y

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