Analysis of SSIM based Quality Assessment across Color Channels of Images

Chandrakanth, T.; Sandhya, B.

doi:10.4018/ijsda.2015070102

Cited by 2 publications

(2 citation statements)

References 25 publications

(20 reference statements)

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“…In precision electronic FICS defect detection, FICS images require strict line-width and line distance, so the Edge Preserving Index (EPI) is used as the third quality assessment indicator. SSIM [36][37][38] measures the similarity between two images and belongs to the objective evaluation of the image. SSIM uses the mean, standard deviation and variance as estimates of brightness, contrast and structural similarity, respectively.…”

Section: Evaluation Standard Of Image Qualitymentioning

confidence: 99%

An novel image de-noising model based on gradient and adaptive curvature features and its application

Huang¹,

Hu²

2019

Phys. Scr.

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In this paper, an image de-noising model with gradient and adaptive curvature features is proposed for the visual inspection of the appearance defects of high-density flexible integrated circuit package substrates with strict line-width and line distance. Firstly, the model proposed in this paper adaptively adjusts the weight of the level set curvature feature and the gradient feature of the image, and uses more abundant first-order differential and second-order differential information of the image as the detection factor for image de-noising. Secondly, theoretical analysis shows that the diffusion intensity of the model in the flat region is larger than that of the classical model, and the de-noising effect is better. Furthermore, at the corners and peaks of the image, the proposed model can suppress the reduction of the gray value and preserve more detailed information of the image and edges. Finally, the experimental analysis shows that the proposed model has the best de-noising effect compared with other models. The method proposed in this paper can effectively remove the noise of the image of the high-density flexible integrated circuit package substrate, and at the same time retain more original details and edge information of the image which has practical significance.

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Section: Evaluation Standard Of Image Qualitymentioning

confidence: 99%

An novel image de-noising model based on gradient and adaptive curvature features and its application

Huang¹,

Hu²

2019

Phys. Scr.

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show abstract

“…Structural similarity (SSIM) [31], [32], peak signal-to-noise ratio (PSNR) [29], [30] and mean squared error (MSE) are generally used as objective quality assessments of the image. In the precision electronic defect detection, since the FICS image has strict requirements on the line width and line distance, the edge-preserving index (EPI) [33], [34] is also required as the third quality evaluation standard for the de-noised image.…”

Section: B Evaluation Standard Of Image Qualitymentioning

confidence: 99%

Research on Image Smoothing Diffusion Model With Gradient and Curvature Features

Huang

2019

IEEE Access

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In this paper, two image smoothing models are proposed for the visual inspection of highdensity flexible IC package substrates with strict requirements on line width and line distance which are applied to the de-noising of high-density flexible IC package substrate images. First of all, the two models proposed in this paper combines the level set curvature feature of the image with gradient threshold, using more abundant second-order differential information as the detection factor to remove the noise in the image. Second, the theoretical analysis shows that the de-noised image obtained by the two models proposed can retain more detailed texture information and edge information of the original image. What is more, the experimental analysis shows that the proposed models have the highest structural similarity and peak signal-to-noise ratio, and have a relatively high edge-preserving index and the lowest mean squared error compared with other models. In particular, the de-noised image through Model 1 has the highest structural similarity and peak signal-to-noise ratio, as well as the lowest mean squared error. The de-noised image through Model 2 has a relatively high edge retention index. The methods proposed in this paper can effectively remove the noise of the image of the high-density flexible IC package substrate and can retain the original details and edges information of the image. INDEX TERMS Curvature, image de-noising, gradient, flexible integrated circuit substrate image.

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Analysis of SSIM based Quality Assessment across Color Channels of Images

Cited by 2 publications

References 25 publications

An novel image de-noising model based on gradient and adaptive curvature features and its application

An novel image de-noising model based on gradient and adaptive curvature features and its application

Research on Image Smoothing Diffusion Model With Gradient and Curvature Features

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