Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks

Yan, Ruomei; Shao, Ling

doi:10.5244/c.27.70

Cited by 21 publications

(11 citation statements)

References 28 publications

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“…This scheme has also analyzed the alpha channel information and classifies the blur type into defocus blur and motion blur categories. Yan and Shao [17] made an attempt to find a general feature extractor for common blur kernels with various parameters, which is closer to realistic application scenarios and applied deep belief neural networks for discriminative learning. In this method Fourier spectrum of blurred images is passed to the neural network as input.…”

Section: Blur Detection and Classificationmentioning

confidence: 99%

Blur Classification Using Segmentation Based Fractal Texture Analysis

Tiwari

2018

IJEEI

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The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a degradation available in hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method than other methods.

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Section: Blur Detection and Classificationmentioning

confidence: 99%

Blur Classification Using Segmentation Based Fractal Texture Analysis

Tiwari

2018

IJEEI

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“…While some methods directly classify the blur without PSF estimation or de-blurring [6]. Reference [7] classifies blurred images based on adaptive dictionary and [8,9] utilize neural networks and other learned classifiers [10] for blur detection and classification. Reference [8] uses statistical features and classifies blur with feed forward neural network.…”

Section: Introductionmentioning

confidence: 99%

Invariants based blur classification algorithm

Gajjar

Zaveri

Shukla

2015

2015 5th Nirma University International Conference on Engineering (NUiCONE)

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Extraction of information from an image acquired by real imaging systems is a difficult task, since the observed image may be degraded by blurring. In this paper, a framework for classification of blur in an image is presented and a technique for classification of blur using invariants is proposed. In this method, the blur classification is carried out without estimating the blurring function. The proposed technique is applied on a large dataset of images degraded by motion blur, Gaussian blur and defocus blur. The simulation results show that the proposed method gives accurate classification of the blur present in an image.

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“…In this regard, learning-based methods are employed because of their adaptive capability [23,24]. Nonetheless, such techniques can offer only a linear solution, whereas real noise models are mainly nonlinear [25].…”

Section: Introductionmentioning

confidence: 99%

Denoising of natural images through robust wavelet thresholding and genetic programming

et al. 2016

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Digital images play an essential role in analysis tasks that can be applied in various knowledge domains, including medicine, meteorology, geology, and biology. Such images can be degraded by noise during the process of acquisition, transmission, storage, or compression. The use of local filters in image restoration may generate artifacts when these filters are not well adapted to the image content as a result of the heuristic optimization of local filters. Denoising methods based on learning procedure are more capable than parametric filters for addressing the conflicts between noise suppression and artifact reduction. In this study, we present a nonlinear filtering method based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises. In the switching scheme, two cascaded detectors are used to detect noise, and two corresponding estimators are employed to effectively and efficiently filter the noise in an image. In the process of training, a method according to patch clustering is utilized, and genetic programming (GP) is subsequently applied to determine the optimum filter (wavelet-domain filter) for each individual cluster, while B Asem Khmag in testing part, the optimum filter trained beforehand by GP is recovered and used on the inputted corrupted patch. This adaptive structure is employed to cope with several noise types. Experimental and comparative analysis results show that the denoising performance of the proposed method is superior to that of existing denoising methods as per both quantitative and qualitative assessments.

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Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks

Cited by 21 publications

References 28 publications

Blur Classification Using Segmentation Based Fractal Texture Analysis

Blur Classification Using Segmentation Based Fractal Texture Analysis

Invariants based blur classification algorithm

Denoising of natural images through robust wavelet thresholding and genetic programming

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