An improved Richardson-Lucy algorithm for single image deblurring using local extrema filtering

Wu, Jiunn-Lin; Chang, Chia-Feng; Chen, Chun-Shih

doi:10.1109/ispacs.2012.6473447

Cited by 15 publications

(15 citation statements)

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“…Blind deconvolution techniques in general underwater settings have been used with success in (Fan et al, 2010a), (Fan et al, 2010b) and with the adapted Richardson-Lucy algorithm in (Wu et al, 2013), while the application on bubbly flow images is novel.…”

Section: Cameramentioning

confidence: 99%

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Zelenka

Koch

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Marine gas seeps, such as in the Panarea area near Sicily (McGinnis et al., 2011), emit large amounts of methane and carbon-dioxide, greenhouse gases. Better understanding their impact on the climate and the marine environment requires precise measurements of the gas flux. Camera based bubble measurement systems suffer from defocus blur caused by a combination of small depth of field, insufficient lighting and from motion blur due to rapid bubble movement. These adverse conditions are typical for open sea underwater bubble images. As a consequence so called 'bubble boxes' have been built, which use elaborate setups, specialized cameras and high power illumination. A typical value of light power used is 1000W (Leifer et al., 2003). In this paper we propose the compensation of defocus and motion blur in underwater images by using blind deconvolution techniques. The quality of the images can be greatly improved, which will relax requirements on bubble boxes, reduce their energy consumption and widen their usability.

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Section: Cameramentioning

confidence: 99%

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Zelenka

Koch

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It is blurring of an image due to movement of the object or imaging system [4]. It is the apparent fast moving objects in an image.…”

Section: Motion Blurmentioning

confidence: 99%

“…Where, +1 is the new approximation from the previous , g is the captured blurred image, n is the number of the iterations, H is PSF and in the first iteration, is same as blurred image g. It minimizes the difference between the blurred image and the predicted image according to the Poisson statistics [4]. The drawback of this algorithm is the …”

Section: Iterative Richardson-lucy Algorithmmentioning

confidence: 99%

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A Survey on Different Image Deblurring Techniques

Vankawala¹,

Ganatra²,

Patel³

2015

IJCA

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Image blur is general artifacts in digital image processing and it is hard to avoid. Image enhancement or deblurring is necessary to reduce blur amount from the image. Image deblurring is a process used to reduce the blur quantity in a blurred image and make the degraded image into sharpened and clear image. When deblurring images, cause of blurring is very important to increase the effect of the deblurring to get good result. While working with real-time images, we may not have the knowledge of the reason of blurring. There are various sources why image gets blurred like motion blur, camera shake, out of focus blur, etc. This paper carried out performance comparison of different techniques to diminish the effects of above mentioned causes of blurring. The analysis and comparison was conceded out based on types of blur, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE) and Execution time.

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“…But they do have overlapping areas. There are several restoration techniques [5] [6] and can be broadly classified into Blind and Non-Blind techniques. Blind techniques try to restore images without having any knowledge of the blur operator (PSF).…”

Section: Introductionmentioning

confidence: 99%

Implementing Blind De-convolution with Weights on X-ray Images for Lesser Ringing Effect

Gupta¹,

Porwal²

2016

IJIGSP

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Abstract-X-rays and other medical images are distorted because of the limitations of the Imaging system. The other source from where the distortions get in are the transmission channels. The distortions are generally noise and blur. Unless and until the medical images are free of noise and blur they cannot be used by medical professionals to the full extent for diagnosis purpose. Therefore these images must be restored properly before they are used for diagnosis purpose. There are different restoration techniques out of which one is Blind Image Deconvolution. X-ray images restored with this technique have ringing effect in them. Using edgetaper (matlab function) prior to Blind Image Deconvolution reduces the ringing effect to an extent. This paper presents Blind Deconvolution algorithm with weights which gives lesser ringing effect in X-ray images when they are restored.

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An improved Richardson-Lucy algorithm for single image deblurring using local extrema filtering

Cited by 15 publications

References 20 publications

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

A Survey on Different Image Deblurring Techniques

Implementing Blind De-convolution with Weights on X-ray Images for Lesser Ringing Effect

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