A weighted discriminative approach for image denoising with overcomplete representations

Adler, Amir; Hel-Or, Yacov; Elad, Michael

doi:10.1109/icassp.2010.5494973

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Selain untuk mendapatkan informasi yang terdapat pada citra, pengolahan citra biasanya digunakan untuk menghasilkan kualitas citra yang lebih baik [16]. Menurut [17], pengolahan citra dibagi menjadi tiga jenis.…”

Section: Image Processingunclassified

Pembangkitan Citra Time Exposure Menggunakan Filter Median

Agustina¹,

Widyantara²,

H³

2021

SPEKTRUM

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Time exposure (timex) image is a type of image generated from image acquisition at acertain time or image acquisition with different exposure criteria, the advantage of timex imageis High Dynamic Range which can help provide details in a digital image processing. Toproduce a good timex image, a method is needed that is able to create a timex image fromeither a video or photo source. There are several methods used to produce timex images,namely Gradient-Based Synthesized, Multi-Exposure Image, and Median Filter. In this study,the three methods are compared in producing timex images with input in the form of imageswith over exposure and low exposure as well as video images. For testing the timex image, theHistogram, Standard Deviation, Variance, Mean, Median, and Mode parameters are used.Based on the results of research conducted on timex images with the Median Filter method, thestandard deviation value is 0.0739 and the variance value is 0.0054 where this value indicatesthat the intensity distribution on the median timex filter image has a wider dynamic range thanthe other two methods. The comparison of the timex image with the fps variation shows that thehigher the fps used, the better the timex image.

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Section: Image Processingunclassified

Pembangkitan Citra Time Exposure Menggunakan Filter Median

Agustina¹,

Widyantara²,

H³

2021

SPEKTRUM

View full text Add to dashboard Cite

show abstract

“…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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“…Shang and Huang [25] proposed a method for denoising using extended nonnegative sparse coding neural network shrinkage algorithm. Adler et al [26] proposed a new approach that optimizes the shape of the shrinkage functions and maximizes denoising performance by emphasizing the contribution of sparse overcomplete representations. Liu and Liu [1] have proposed image denoising based on diffusion tensors.…”

Section: Introductionmentioning

confidence: 99%

Wavelet and FFT Based Image Denoising Using Non-linear Filters

Gopinathan

Kokila

Thangavel

2015

IJECE

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We propose a stationary and discrete wavelet based image denoising scheme and an FFTbased image denoising scheme to remove Gaussian noise. In the first approach, high subbands are added with each other and then soft thresholding is performed. The sum of low subbands is filtered with either piecewise linear (PWL) or Lagrange or spline interpolated PWL filter. In the second approach, FFT is employed on the noisy image and then low frequency and high frequency coefficients are separated with a specified cutoff frequency.Then the inverse of low frequency components is filtered with one of the PWL filters and the inverse of high frequency components is filtered with soft thresholding. The experimental results are compared with Liu and Liu's tensor-based diffusion model (TDM) approach.

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A weighted discriminative approach for image denoising with overcomplete representations

Cited by 9 publications

References 4 publications

Pembangkitan Citra Time Exposure Menggunakan Filter Median

Pembangkitan Citra Time Exposure Menggunakan Filter Median

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

Wavelet and FFT Based Image Denoising Using Non-linear Filters

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