TY - JOUR
T1 - Clustering-Based 3-D-MAP Despeckling of SAR Images Using Sparse Wavelet Representation
AU - Aranda-Bojorges, Gibran
AU - Ponomaryov, Volodymyr
AU - Reyes-Reyes, Rogelio
AU - Sadovnychiy, Sergiy
AU - Cruz-Ramos, Clara
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum a posteriori (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception.
AB - Image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum a posteriori (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception.
KW - Clustering methods
KW - filtering
KW - image processing
KW - maximum a posteriori (MAP) estimator
KW - speckle
KW - synthetic aperture radar (SAR)
KW - wavelet transforms
UR - http://www.scopus.com/inward/record.url?scp=85114732934&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2021.3108774
DO - 10.1109/LGRS.2021.3108774
M3 - Artículo
AN - SCOPUS:85114732934
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -