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SEM Image Quality Assessment Based on Texture Inpainting Zhaolin LU Ziyan ZHANG Yi WANG Liang DONG Song LIANG Publication IEICE TRANSACTIONS on Information and Systems Vol.E104-D No.2 pp.341-345 Publication Date: 2021/02/01 Publicized: 2020/10/30 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2020EDL8123 Type of Manuscript: LETTER Category: Image Processing and Video Processing Keyword: image quality assessment, no-reference, SEM image, texture-inpainting, Full Text: PDF(2.3MB)>>
Summary: This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method. | ||||||||||
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