Gradient nuclear norm minimization-based image filter
| Autor: | Saurabh Khare, Praveen Kaushik |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Nuclear norm minimization
Computer science High density Statistical and Nonlinear Physics 02 engineering and technology Condensed Matter Physics 01 natural sciences Composite image filter Image (mathematics) Noise Computer Science::Computer Vision and Pattern Recognition 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution 010306 general physics Algorithm Degradation (telecommunications) |
| Zdroj: | Modern Physics Letters B. 33:1950214 |
| ISSN: | 1793-6640 0217-9849 |
| Popis: | Designing an efficient filtering technique is an ill-posed problem especially for image affected from high density of noise. The majority of existing techniques suffer from edge degradation and texture distortion issues. Therefore, in this paper, an efficient weighted nuclear norm minimization (NNM)-based filtering technique to preserve the edges and texture information of filtered images is proposed. The proposed technique significantly improves the quantitative improvements on the low rank approximation of nonlocal self-similarity matrices to deal with the overshrink problem. Extensive experiments reveal that the proposed technique preserves edges and texture details of filtered image with lesser number of visual artifacts on visual quality. The proposed technique outperforms the existing techniques over the competitive filtering techniques in terms of structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR) and edge preservation index (EPI). |
| Databáze: | OpenAIRE |
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