An efficient deconvolution technique by identification and estimation of blur

Chokshi, Rikita; Israni, Dippal; Chavda, Nishidh

doi:10.1109/rteict.2016.7807773

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Blind deconvolution methods are used to deblur the images in [25][26][27][28]. [29] uses non blind methods of deconvolution which includes Lucy-Richardson method [30], Wiener filtering [31], Regularization approach [32], etc.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Sree,

Nagajyothi,

Revathi

et al. 2023

IJRITCC

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Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages. Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision.

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Section: Related Workmentioning

confidence: 99%

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Sree,

Nagajyothi,

Revathi

et al. 2023

IJRITCC

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“…To summarize, the SUSAN algorithm is basically a combination of a gauss filter and a linear filter that prevents image degradation. Linear filters, in general, tend to blur more image details than most non-linear filters [14]. As a result of our applying of the SUSAN filter to the motifs we obtained, the RGB pictures were converted into grayscale.…”

Section: The Susan Filtermentioning

confidence: 99%

SARS-CoV-2 Virus RNA Sequence Classification and Geographical Analysis with Convolutional Neural Networks Approach

Yazar

2022

EJT

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Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method.

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