A new method for noise estimation in images

Bilcu, Radu Ciprian; Vehviläinen, Markku

doi:10.1109/nsip.2005.1502258

Cited by 44 publications

(16 citation statements)

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“…In Block based approach image [12], [22] is tessellated into number of non-overlapping, homogenous blocks. Assume that each block is contains noise.…”

Section: Introductionmentioning

confidence: 99%

Block Based SVD approach for Additive White Gaussian Noise level Estimation in Satellite Images

Kumar

Sreenivasulu

Rajan

2016

IJMTER

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Abstract-In this digital era, estimation of noise and de-noise of digital image is one of the charttopping fields. In real time, noise may happen at any stage starts from capturing to reception. Noise estimation is ever challenging field even till now. Additive White Gaussian Noise is the basic type when dealing with images on statically way of studying. So, In this paper, as a first step we proposed an effective technique for the estimation of noise in a satellite image by using Block based SVD approach. This paper addresses the following steps 1.Noise estimation by trail method of singular values 2) Corrupting the image by known noise and study the change 3) Divide the whole image into blocks and add the known noise to the image and finally retrieve the effect on original image. It is one of best techniques for estimating AWGN in a real-time.

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“…In Block based approach image [12], [22] is tessellated into number of non-overlapping, homogenous blocks. Assume that each block is contains noise.…”

Section: Introductionmentioning

confidence: 99%

Block Based SVD approach for Additive White Gaussian Noise level Estimation in Satellite Images

Kumar

Sreenivasulu

Rajan

2016

IJMTER

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“…(b) Block-based approach, in which the variance over a set of blocks of the image is calculated and the average of the smallest block variances is taken as an estimate of the global noise variance [16]. Liu et al [17] presented a fast and efficient noise variance estimation algorithm through a window and the corresponding operation of computing variance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Structure-Based Gaussian Noise Variance Estimation Method for Noisy Images

Shimamura

2013

Journal of Signal Processing

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Noise can significantly impact the effectiveness of digital image processing. In this paper, an improved structurebased Gaussian noise variance estimation algorithm is presented. This method first separates the image into blocks and calculates homogeneity measures of every block through the proposed masks, taking the image structure into account. Then, the most homogeneous blocks are selected using a new threshold. Finally, pixel value variances of all selected blocks are averaged to estimate the global noise variance for one image. Comparative experiments with a variety of images using the proposed method and original structure-oriented method are described, and the experimental results show that the proposed method is feasible and effective, especially for good-quality images.

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“…Olsen [16] gave a complete description and comparison of six earlier estimation algorithms. They are classified into two different approaches [17][18][19][20]: filter-based (or smoothingbased) and block-based. In filter-based methods, the noisy image is first filtered by a low-pass filter to suppress the image structures.…”

Section: Introductionmentioning

confidence: 99%

Bistate speckle removal filter based on noise variance

Vasuki

Roomi

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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In this paper the proposed Bistate filter to denoised the speckle noise affected images. The denoising is achieved by understanding the performance of various speckle filters and selecting appropriate filter to remove speckle for a given arbitrary noisy image. The selection of the either of the filter is achieved by estimating variance of the noisy image. The results show that the proposed Bistate filter chooses lee or anisotropic diffusion filter to despeckled noisy image based on noise variance.

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A new method for noise estimation in images

Cited by 44 publications

References 0 publications

Block Based SVD approach for Additive White Gaussian Noise level Estimation in Satellite Images

Block Based SVD approach for Additive White Gaussian Noise level Estimation in Satellite Images

An Improved Structure-Based Gaussian Noise Variance Estimation Method for Noisy Images

Bistate speckle removal filter based on noise variance

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