Image quality assessment using edge based features

Attar, Abdolrahman; Shahbahrami, Asadollah; Rad, Reza Moradi

doi:10.1007/s11042-015-2663-9

Cited by 25 publications

(10 citation statements)

References 35 publications

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“…In order to handle these cases, to create a decision-making model, a simple check between two successive images by Structural SIMilarity (SSIM ) index is used. SSIM basically shows the degree of similarity of two images, scaled on the range 0 ≤ SSIM ≤ 1, the higher the value of SSIM , the more similarity there is [30], [31]. Therefore, the registration method is applied to those image pairs whose SSIM is greater than a threshold (th).…”

Section: Methodology and Contributionmentioning

confidence: 99%

2D reconstruction of small intestine's interior wall

Attar

Xie

Wang

et al. 2019

Computers in Biology and Medicine

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Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets -one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively -registration residual error of 0.93±0.33 pixels on 2500 real endoscopy image pairs and residual error accumulation of 16.59 pixels and without affecting the visual registration quality on stitching 152 images of Micro-Ball.

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Section: Methodology and Contributionmentioning

confidence: 99%

2D reconstruction of small intestine's interior wall

Attar

Xie

Wang

et al. 2019

Computers in Biology and Medicine

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“…Several methods thus consider edge features as the basis for quality assessment (Table 1). Some calculate edge features, including number, length, direction, strength, contrast, and width, and compare them using the similarity measure to estimate image or video quality (Attar et al, 2016;Ni et al, 2017;Yang et al, 2019b).…”

Section: Related Workmentioning

confidence: 99%

ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution

Kirillova¹,

Eugene²,

Antsiferova³

et al. 2021

Preprint

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Despite the growing popularity of video super-resolution (VSR), there is still no good way to assess the quality of the restored details in upscaled frames. Some SR methods may produce the wrong digit or an entirely different face. Whether a method's results are trustworthy depends on how well it restores truthful details. Image super-resolution can use natural distributions to produce a high-resolution image that is only somewhat similar to the real one. VSR enables exploration of additional information in neighboring frames to restore details from the original scene. The ERQA metric, which we propose in this paper, aims to estimate a model's ability to restore real details using VSR. On the assumption that edges are significant for detail and character recognition, we chose edge fidelity as the foundation for this metric. Experimental validation of our work is based on the MSU Video Super-Resolution Benchmark, which includes the most difficult patterns for detail restoration and verifies the fidelity of details from the original frame. Code for the proposed metric is publicly available at https://github.com/msu-video-group/ERQA. a https://orcid.

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“…Patches failing to pass the QC filter were ignored. The filter is a function that calculates edges from the red color channel using Sobel edge detection and was inspired by previous works [1,2,30]. The Sobel edges were then thresholded at 0.05, and surviving edges were summed to obtain a total edge metric for the patch.…”

Section: Quality Controlmentioning

confidence: 99%