SAR Image despeckling via sparse representation

Zhongmei Wang; Xiaomei Yang; Liang Zheng

doi:10.1117/12.2069168

19 November 2014 SAR Image despeckling via sparse representation

Zhongmei Wang, Xiaomei Yang, Liang Zheng

Proceedings Volume 9264, Earth Observing Missions and Sensors: Development, Implementation, and Characterization III; 92641O (2014) https://doi.org/10.1117/12.2069168
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.

Citation Download Citation

Zhongmei Wang, Xiaomei Yang, and Liang Zheng "SAR Image despeckling via sparse representation", Proc. SPIE 9264, Earth Observing Missions and Sensors: Development, Implementation, and Characterization III, 92641O (19 November 2014); https://doi.org/10.1117/12.2069168

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KEYWORDS

Associative arrays

Synthetic aperture radar

Image filtering

Image quality

Chemical species

Denoising

Image compression

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