Poisson image denoising using fast discrete curvelet transform and wave atom

Palakkal, Sandeep; Prabhu, K.M.M.

doi:10.1016/j.sigpro.2012.01.008

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…MS-VSTþFDCT results in lower PSNR values, when compared to the (g-)platelet and (g-)quadlet algorithms. However, we note that MS-VSTþFDCT algorithm may perform better than platelets when the image consists of large number of discontinuities [20]. PURE-LET algorithm results in low PSNR values on images Lena, Peppers and Cameraman.…”

Section: Resultsmentioning

confidence: 81%

“…As (g-) platelet and (g-)quadlet algorithms are not shift-invariant, we also used 25 cycle spins (CSp) [25] to improve the performance. For MS-VST þFDCT, we used four scales of FDCT, by setting the number of directional subbands to 8, 16, 16 and 32, from coarse to fine scales; other parameter values were: FDR ¼ 10 À3 and the number of iterations of HSD was 5 [20]. We used PURE-LET with interscale amplitude-sign dependencies (i.e., PURE-LET2), with five scales for Haar wavelet transform [13] and 25 CSp.…”

Section: Resultsmentioning

confidence: 99%

“…Apart from platelet algorithm, other recent algorithms for Poisson image denoising include PURE-LET [13,18] and MS-VST algorithms [19,20]. PURE-LET algorithm has been developed in the Haar wavelet domain to handle specifically the Poisson noise.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, MS-VST approximates the Poisson noise by a Gaussian model using a variance stabilizing transform (VST) and applies Gaussian denoising algorithm in a multiscale transform domain. For example, MS-VST þFDCT [20] uses the fast discrete curvelet transform (FDCT) [21] to establish noise removal. However, the Gaussian approximation is often not perfect, especially when the image intensity is very low.…”

Section: Introductionmentioning

confidence: 99%

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Poisson image denoising using geometric platelets and geometric quadlets

Palakkal

Prabhu

2013

Signal Processing

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Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Poisson image denoising using geometric platelets and geometric quadlets

Palakkal

Prabhu

2013

Signal Processing

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“…A famous technique for Poisson denoising is using a variance stabilizing transform (VST) to transform the noise model from Poisson to Gaussian [10]. Different famous examples for VST are Anscombe's VST [11], and the Haar-Fisz transform [12], which combines Haar wavelet transform with the Fisz transform [13].…”

Section: Poisson Noisementioning

confidence: 99%

A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise

et al. 2016

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Original scientific paperWe propose a Weighted Gradient Filter for denoising of Poisson noise in medical images. In a predefined window, gradient of the centre pixel is averaged out. Gaussian Weighted filter is used on all calculated gradient values. Proposed method is applied on biomedical images X-Rays and then on different general images of LENA and Peppers. Recovery results show that the proposed weighted gradient filter is efficient and has better visual appearance. Moreover, proposed method is computationally very efficient and faster than Non Local Mean (NLM) filter which is an advanced technique for Poisson noise removal. Proposed method results are also better in terms of performance measures parameters i.e. correlation, Peak Signal-to-Noise Ratio (PSNR), Maximum Structural Similarity Index Measure (MSSIM) and Mean Square Error (MSE) than the conventional Median, Wiener and NLM filter. Keywords: denoising; image filtering; image processing; medical images; Poisson noise Primjena ponderiranog stupnjevanog filtra u izoštravanju rendgenskih slika uz postojanje Poissonova šumaIzvorni znanstveni članak Predlažemo ponderirani stupnjevani filtar za otklanjanje Poissonova šuma na rendgenskim slikama. U unaprijed definiranom prozoru izračunat je gradijent središnjeg piksela. Za izračunavanje vrijednosti gradijenta primijenjen je Gaussov ponderirani filtar. Predložena metoda je primijenjena na biomedicinske rendgenske slike, a zatim na različite uobičajene slike LENE i paprika. Rezultati pokazuju učinkovitost i bolju jasnoću slika uz primjenu ponderiranog stupnjevanog filtra. Uz to, predložena metoda je računalno vrlo učinkovita i brža od Non Local Mean (NLM) filtra koji predstavlja unaprijeđenu metodu za otklanjanje Poissonova šuma. Rezultati predložene metode su također bolji u odnosu na parametre za mjerenje performanse t.j. korelacije, Peak Signal-to-Noise Ratio (PSNR), Maximum Structural Similarity Index Measure (MSSIM) i Mean Square Error (MSE) nego uobičajeni Median, Wiener i NLM filter.

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Rapid Medical Images Restoration Combining Parametric Wiener Filtering and Wave Atom Transform Based on Local Adaptive Shrinkage

Mbarki

Seddik

2021

Advanced Information and Knowledge Processing

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Poisson image denoising using fast discrete curvelet transform and wave atom

Cited by 19 publications

References 27 publications

Poisson image denoising using geometric platelets and geometric quadlets

Poisson image denoising using geometric platelets and geometric quadlets

A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise

Rapid Medical Images Restoration Combining Parametric Wiener Filtering and Wave Atom Transform Based on Local Adaptive Shrinkage

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