Edge Strength Similarity for Image Quality Assessment

Zhang, Xuande; Feng, Xiaoyi; Wang, Weiwei; Xue, Wufeng

doi:10.1109/lsp.2013.2244081

Cited by 142 publications

(91 citation statements)

References 10 publications

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“…On the contrary, we determine the informative patches within the blurry image using mathematical equations for characteristics, edge strength [17] and uniformity of edge direction. Motivated by the structure of the edge strength used in [17] for image quality assessment, we propose a directional edge strength map and uniformity map described as follows. Denote the observed blurry image as 1 [ , , , , ] ,…”

Section: Overall Algorithmmentioning

confidence: 99%

Fast Patch-based De-blurring with Directional-oriented Kernel Estimation

Min¹,

Chong²

2017

Journal of IKEEE

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This paper proposes a fast patch-based de-blurring algorithm including kernel estimation based on the angle between the edge and the blur direction. For de-blurring, image patches from the most informative edges in the blurry image are used to estimate a kernel with low computational cost. Moreover, the kernels of each patch are estimated based on the correlation between the edge direction and the blur direction. This makes the final kernel more reliable and creates an accurate latent image from the blurry image. The combination of directionally oriented kernel estimation and patch-based de-blurring is faster and more accurate than existing state-of-the art methods. Experimental results using various test images show that the proposed method achieves its objectives: speed and accuracy. key words: de-blurring, edge direction, image restoration, kernel estimation, motion blur.

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Section: Overall Algorithmmentioning

confidence: 99%

Fast Patch-based De-blurring with Directional-oriented Kernel Estimation

Min¹,

Chong²

2017

Journal of IKEEE

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“…To overcome the limitations of pixel difference based quality metrics, structural assessment based quality metrics were introduced such as structural similarity index (SSIM) [2], structural content (SC), complex wavelet structural similarity index (CWSSIM) [3], feature similarity index (FSIM) [4] and edge strength similarity index (ESSIM) [5], etc. Out of which structural similarity index became much popular because of its accuracy to determine quality of the image.…”

Section: Literature Surveymentioning

confidence: 99%

“…In reference paper [4], authors believe that human vision system interpret image according to low level images and they have used phase congruency as main feature in their work. ESSIM in [5] considers anisotropic regularity and irregularity of edge into proposed metric. This quality metric could be classified as edge based quality metric too.…”

Section: Literature Surveymentioning

confidence: 99%

“…Also, authors in [8] proved that universal image quality index (UIQI) is superior metric than MSE with simple mathematical model. Literatures [1][2][3][4][5][6][7][8] represent variety of quality metrics but the common consideration for their design is Gaussian noise or Gaussian distortion. As per our knowledge, we come across only one paper in which quality metric is specifically designed for speckle noise.…”

Section: Literature Surveymentioning

confidence: 99%

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Identification of Suited Quality Metrics for Natural and Medical Images

Thakur¹,

Damodare²,

Sapkal³

2016

SIPIJ

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“…The SGF is used particularly to get edge information at different orientations and also to get more degree of freedom in selecting the edges in the image. The application of steerable filter in IQA is motivated by the characteristics of edges as given in [18] which are (i) Local regularity/ smoothness / continuity and local irregularity /oscillation/ discontinuity which are also orthogonal to each other. (ii) There also exists anisotropic structures in the image and the edges always stretch out in multiple directions.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Assessment Using Edge Correlation

Ruikar

Sinha

Chaudhury

2017

International Journal of Electronics and Telecommunications

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Abstract-In literature, oriented filters are used for low-level vision tasks. In this paper, we propose use of steerable Gaussian filter in image quality assessment. Human visual system is more sensitive to multidirectional edges present in natural images. The most degradation in image quality is caused due to its edges. In this work, an edge based metric termed as steerable Gaussian filtering (SGF) quality index is proposed as objective measure for image quality assessment. The performance of the proposed technique is evaluated over multiple databases. The experimental result shows that proposed method is more reliable and outperform the conventional image quality assessment method.

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Edge Strength Similarity for Image Quality Assessment

Cited by 142 publications

References 10 publications

Fast Patch-based De-blurring with Directional-oriented Kernel Estimation

Fast Patch-based De-blurring with Directional-oriented Kernel Estimation

Identification of Suited Quality Metrics for Natural and Medical Images

Image Quality Assessment Using Edge Correlation

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