An algorithm for improving Non-Local Means operators via low-rank\n approximation

2014/11/20 by Victor May, Yosi Keller, May, Victor +5
Computer Science · Engineering · Mathematics · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #General Mathematics (math.GM) #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #Statistical and numerical algorithms

paper · pdf · doi:10.48550/arxiv.1412.2067

openalex publication_date 2014/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a method for improving a Non Local Means operator by computing its\nlow-rank approximation. The low-rank operator is constructed by applying a\nfilter to the spectrum of the original Non Local Means operator. This results\nin an operator which is less sensitive to noise while preserving important\nproperties of the original operator. The method is efficiently implemented\nbased on Chebyshev polynomials and is demonstrated on the application of\nnatural images denoising. For this application, we provide a comprehensive\ncomparison of our method with leading denoising methods.\n

Citations

• Is Gauss Quadrature Better than Clenshaw–Curtis?
• Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

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