SEM Image Quality Assessment Based on Texture Inpainting

Lu, Zhaolin; Zhang, Ziyan; Wang, Yi; Liang, Dong; Song, Liang

doi:10.1587/transinf.2020edl8123

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…Therefore, starting with the sensitive characteristics of human visual system, evaluating the distortion quality of computer network shared pictures through intelligent algorithms, and then helping to improve the quality of network shared pictures is one of the important directions of future development. The perfect and efficient network shared picture distortion quality evaluation system can not only quickly reflect the real quality of the picture, but also make a quantitative output measurement for the output of the codec, ensure the service quality to users, and help to design and optimise the display system in line with the sensitive characteristics of human vision (Chen et al, 2021;Zhaolin et al, 2021;Bouida et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Research on distortion quality evaluation of computer network shared image based on visual sensitivity

Li¹

2023

IJWMC

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Shared image distortion will affect the user's experience, and then damage people's life and entertainment experience. In view of this, this research starts with the evaluation and classification of network shared image distortion quality, improves the shared image distortion quality evaluation algorithm combined with the sensitive characteristics of human vision, and verifies its performance superiority through comparative experiments. The results show that the performance of some improved reference quality evaluation algorithms reaches the highest values, which are 0.7923, 0.3224, 0.7931 and 0.8213, respectively. The improved non-reference quality evaluation algorithm achieves the highest values of positive indicators in the comparison of performance values, which are 0.487 and 0.287, respectively, while the lowest value of negative indicators is 0.902. It can be seen that the improved shared image quality evaluation algorithm conforms to the sensitive characteristics of human eyes, has high computational efficiency and has broad application prospects.

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Section: Introductionmentioning

confidence: 99%

Research on distortion quality evaluation of computer network shared image based on visual sensitivity

Li¹

2023

IJWMC

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“…As far as we know, few studies are available on the SEM image quality evaluation at present, and they only focus on specific distortion types [6], [7]. These methods have difficulty applying SEM images with unknown or mixed distortion types or need to rely on pre-tasks [8]. Therefore, the research on NR-IQA methods for SEM image distortion feature design is urgent topic to be studied deeply.…”

Section: Introductionmentioning

confidence: 99%

“…First, the dark channel image of the SEM image is generated to extract its edge, and then, the noise is eliminated by the filter that preserves the edge. Lu [8] used texture inpainting as the upfront task and transferred the weighting factors obtained from texture inpainting task into the IQA task for assessing SEM image quality. This method makes up for the shortcoming of the abovementioned methods that can only target specific distortion types.…”

Section: Introductionmentioning

confidence: 99%

SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features

Wang

Ding

et al. 2022

IEEE Access

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The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. SEM images obtained by electron beam feedback have more complex texture features than natural images obtained by optical imaging, and this condition results in poor performance of algorithms used for assessing natural image quality on SEM datasets,meanwhile,the field of SEM image quality assessment(IQA) is mostly aimed at specific distortion types. In order to solve the above two problems,to address the rich texture, few edges, and extreme sensitivity to the distortion degree of SEM images, we propose a texture and semantic IQA (TSIQA) method for SEM images based on sparse mask and information entropy increase. First, we construct a neural network containing sparse mask module (SMM), which is used to extract intuitive texture features in the spatial and channel domains. Simultaneously, information growth attention (IGA) is introduced into SMM to detect the difference between current and past features of the network for extracting deep semantic information. The quality assessment experiments on SEM image datasets show that compared with the state-of-the-art IQA methods, including popular noreference techniques adapted to the SEM-IQA, the TSIQA has superiority in typical criteria.

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SEM Image Quality Assessment Based on Texture Inpainting

Cited by 2 publications

References 20 publications

Research on distortion quality evaluation of computer network shared image based on visual sensitivity

Research on distortion quality evaluation of computer network shared image based on visual sensitivity

SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features

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