Blur identification in image processing

Rugna, Jérôme Da; Konik, Hubert

doi:10.1109/ijcnn.2006.247106

Cited by 4 publications

(4 citation statements)

References 23 publications

(15 reference statements)

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“…Teo and Zhan 15 Liu et al 14 Xu et al 11 Rugna and Konik 13 Huang et al 18 Ours It is found that accuracy of these datasets is all greater than 94%, which again demonstrates our method has superior generalization ability and satisfactory robustness. Considering blurry images and sharp images separately, all precision and recall are greater than 94% except GoPro dataset.…”

Section: Methodsmentioning

confidence: 57%

“…Khan et al 12 proposed a blur discrimination method by frequency-based multi-level fusion transformation, which could detect and classify the blur and non-blur by single image processing. Rugna and Konik 13 observed that the blur was insensitive to low-pass filtering. They utilized this feature to judge whether a given image was blurry or not.…”

Section: Methods Based On Hand-extracted Featuresmentioning

confidence: 99%

“…In the last 20 years or so, several practicable methods are proposed for blur discrimination. [10][11][12][13][14][15][16] Most of them employ a two-step strategy. First, some low-level blur-related features are handcrafted based on various empirical image statistics in gradient, frequency, and other domains.…”

Section: Introductionmentioning

confidence: 99%

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Fusion network for blur discrimination

2021

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Blurry image discrimination is a challenging and critical problem in computer vision.It is useful for image restoration, object recognition, and other image applications. In previous studies, researchers proposed a discrimination method based on hand-extracted features or deep learning. However, these methods are either pure data driven by deep learning or over-simplified assumptions on prior knowledge. As a result, a discrimination method is proposed for distinguishing sharp images and blurry images based on a fusion network. The proposed method can automatically discriminate and detect blur without performing image restoration or blur kernel function estimation. Actually, the blur and the noise are extracted by the improved VGG16 network and texture noise extraction algorithm, respectively. Then the fusion network integrates the advantages of deep learning and hand-extracted features, and achieves ultimate highaccuracy discrimination results. Rigorous experiments performed on own dataset and other popular datasets with a number of blurry images and sharp images, including RealBlur dataset, BSD-B dataset, and GoPro dataset. The results show that the proposed method outperforms with an accuracy of 98% on our own dataset and 94.8% on the other dataset, which satisfies the requirements of the image applications. Similarly, we have compared our method with stateof-the-art methods to show its robustness and generalization ability. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Methodsmentioning

confidence: 57%

Section: Methods Based On Hand-extracted Featuresmentioning

confidence: 99%

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Fusion network for blur discrimination

2021

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“…Aizenberg et al propose a neural network based on multivalued neurons to estimate the PSF [22,23]. Da Rugna and Konik present a supervised learning approach to classify the blurry and sharp regions in an image [24]. Some other techniques for blur identification include the methods based on the autoregressive model [25], kurtosis minimization [26], and the characteristics of the restored image [27].…”

Section: Introductionmentioning

confidence: 99%

Defocus blur parameters identification by histogram matching

Lin

Chou

2012

J. Opt. Soc. Am. A

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A defocus blur identification technique based on histogram analysis of a real edge image is presented. The image defocus process of a camera is formulated by incorporating the nonlinear camera response and the intensity-dependent noise model. The histogram matching between the synthesized and real defocused regions is then carried out with intensity-dependent filtering. By iteratively changing the point-spread function parameters, the best blur extent is identified from histogram comparison. We have performed the experiments on both the synthetic and real edge images. The results have demonstrated the robustness and feasibility of the proposed technique.

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An unsupervised approach to determination of main subject regions in images with low depth of field

Zhang

2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

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In this paper, we propose an unsupervised approach to separate focused main subject regions from defocused background. This algorithm first computes the blurring level using the bivariate kurtosis of all 8×8 DCT blocks of a photographic image with low depth of field. Then these blocks are clustered to blurry regions and sharp regions. The sharp regions are considered the main subject regions. This is a fast unsupervised approach to detect the main subject regions in photographic images with low depth of field. Experimental results show that the presented method provides higher speed than the multiresolution waveletbased segmentation method.

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Blur identification in image processing

Cited by 4 publications

References 23 publications

Fusion network for blur discrimination

Fusion network for blur discrimination

Defocus blur parameters identification by histogram matching

An unsupervised approach to determination of main subject regions in images with low depth of field

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