Blind Adaptive Method for Image Restoration Using Microscanning

Martínez, José Luis López; Kober, Vitaly

doi:10.1587/transinf.e95.d.280

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…[4] The output is calculated using the central pixel value and local statistics, it means that the filtering process is not constant for each pixel. The AWF provides a good performance for the degraded image corrupted by additive white Gaussian noise.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Edge Preserving BIT-Plane Adaptive Wiener Filter for Gaussian Noise Restoration

Agarwal¹

2014

IJCA

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Restoration of images is a significant area of research as presence of noise is unavoidable. Noises are modeled as Gaussian noise predominantly in all fields of image processing on images acquired from all spectrums. The global method of Adaptive Wiener Filter seems to perform the best amongst all the filters available. Numerous studies have established this fact. We have tried to develop a local bitplane Adaptive Wiener Filter Algorithm which performs much better than the traditional filter. The results are not only visually verifiable but the Root Mean Square Error and the Peak Signal to Noise Ratio parameters validate our results. The suggested algorithm is actually a modification of the original one hence the processing time is not significantly effected. The algorithm works because instead of the global method which blurs out the edges, the local bitplane method works on the windows in the bitplane and hence the edges are preserved in a much better way. The results are more dominant in cases with larger variance noise and larger window size which tends to get blurred more in the global method.

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Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Edge Preserving BIT-Plane Adaptive Wiener Filter for Gaussian Noise Restoration

Agarwal¹

2014

IJCA

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“…9,10 Recently, a blind adaptive method based on camera microscanning were proposed for restoration of gray-scale images degraded with multiplicative interference, additive interference and sensor's noise. 11,12 In this paper we present a method to restore color images from a set of degraded color images obtained with a microscanning imaging system. First, we use microscanning to obtain a set of observed degraded color images of the same scene with a controlled shift between the scene and camera.…”

Section: Introductionmentioning

confidence: 99%

Color image restoration based on camera microscanning

2014

Self Cite

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In this work, we propose a method to restore color images from a set of degraded color images obtained with a microscanning imaging system. Using the set of observed images, image restoration is carried out by solving a system of equations that is derived from optimization of an objective function. Since the proposed method possesses a high computational complexity, a fast algorithm is developed. Experimental and computer simulation results obtained with the proposed method are analyzed in terms of restoration accuracy and tolerance to additive input noise.

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“…Blind image restoration problem arises naturally and is expressed as estimate the high resolution image and the PSF of the imaging system simultaneously (Yan, 2001). In image processing area, blind image restoration has always been a challenge and hot discussed problem (Lopez-Martinez and Kober, 2011;Marrugo, 2011;Luo and Fu, 2011;Seghouane, 2011;Giannoula, 2011).…”

Section: Introductionmentioning

confidence: 99%

Blind Image Restoration Based on Signal-to-Noise Ratio and Gaussian Point Spread Function Estimation

Qin¹

2013

RJASET

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In order to improve the quality of restored image, a blind image restoration algorithm is proposed, in which both the Signal-to-Noise Ratio (SNR) and the Gaussian Point Spread Function (PSF) of the degraded image are estimated. Firstly, the SNR of the degraded image is estimated through local deviation method. Secondly, the PSF of the degraded image is estimated through error-parameter method. Thirdly, Utilizing the estimated SNR and PSF, high resolution image is restored through Wiener filtering restoration algorithm. Experimental results show that the quality and peak signal-to-noise of the restored image are better around the real value and justify the fact that the SNR an-d PSF estimation plays great important part in blind image restoration.

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Blind Adaptive Method for Image Restoration Using Microscanning

Cited by 7 publications

References 7 publications

Edge Preserving BIT-Plane Adaptive Wiener Filter for Gaussian Noise Restoration

Edge Preserving BIT-Plane Adaptive Wiener Filter for Gaussian Noise Restoration

Color image restoration based on camera microscanning

Blind Image Restoration Based on Signal-to-Noise Ratio and Gaussian Point Spread Function Estimation

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