A nonlocal approach for SAR image denoising

Parrilli, Sara; Poderico, Mariana; Angelino, Cesario Vincenzo; Scarpa, Giuseppe; Verdoliva, Luisa

doi:10.1109/igarss.2010.5651432

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…Many techniques have been proposed to reduce Gaussian noise. In fact, the application of the wavelet transform with Bayesian technique in image denoising has shown remarkable success over the last decade [5][6][7][8][9]. In additive white Gaussian noise (AWGN) model, this paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

Kittisuwan

2015

Fluct. Noise Lett.

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The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

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Section: Introductionmentioning

confidence: 99%

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

Kittisuwan

2015

Fluct. Noise Lett.

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“…where z(i) is the normalising constant and the parameter h denotes a degree of filtering (Parrilli, Poerico, Angelino, Scarpa, & Verdoliva, 2010). It controls the decay of the exponential function and hence the decay of the weights as a function of the Euclidean distances.…”

Section: Introductionmentioning

confidence: 99%

A fusion approach for denoising impulse noise

Rajamani

Krishnaveni

2015

International Journal of Electronics Letters

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In the image handling process, effective interpretation or reconstruction of the original image is mandatory, since the post-processing of images relies upon this important preprocessing task. It is well recognised that the noise makes degradation in the quality of image, which in turn results poor interpretation of images. This paper targets to remove salt-and-pepper or fixed impulse noise from images by fusing two techniques, namely median and non-local means (MNLM) filtering, and exploits both filtering benefits altogether. In the first phase, median filtering has been applied along with new sorting algorithm instead of the conventional method of sorting in order to reduce computational and hardware complexity. Even though the non-linear median filter performs better for low-level noise density and best in edge preservation, it gives blurring effect in the case of high-level noise density. This paper tries to overcome this limitation by fusing NLM filter effectively during the second phase. This fusion approach has brought promising quantitative results such as higher PSNR (peak signal-to-noise ratio) and lower MSE (mean square error) when compared to other existing standard algorithms such as SMF (standard median filter) and NLM filtering approaches.

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“…Because of the nature of data acquisition, SAR images are inherently affected by speckle noise and artifacts. Image despeckling problem has been well studied over the years [2,3,4,5,6,7,8] and it is still a popular research area thanks to the recently introduced concepts of nonlocal means [9] and dictionary learning [10]. Speckle is often modeled as multiplicative noise given as follows:…”

Section: Introductionmentioning

confidence: 99%

SAR Image despeckling via sparse representation

Wang¹,

Yang²,

Liang³

2014

SPIE Proceedings

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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.

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A nonlocal approach for SAR image denoising

Cited by 31 publications

References 15 publications

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

A fusion approach for denoising impulse noise

SAR Image despeckling via sparse representation

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