Multispectral image denoising with optimized vector non-local mean filter
Author | Ben Said, Ahmed |
Author | Hadjidj, Rachid |
Author | Melkemi, Kamal Eddine |
Author | Foufou, Sebti |
Available date | 2021-04-22T13:00:30Z |
Publication Date | 2016 |
Publication Name | Digital Signal Processing: A Review Journal |
Resource | Scopus |
Abstract | Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with additive white Gaussian noise. PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity. |
Sponsor | This publication was made possible by NPRP grant # 4-1165-2-453 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Elsevier Inc. |
Subject | Bandpass filters Gaussian noise (electronic) Parameter estimation Pixels Restoration White noise Additive White Gaussian noise Multi-spectral imaging systems Multispectral images Non- local means filters Non-local mean filters Optimization framework Stein's unbiased risk estimators (SURE) Unbiased risk estimator Image denoising |
Type | Article |
Pagination | 115-126 |
Volume Number | 58 |
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