Zuozheng Lian1, Hong Zhao2,*, Qianjun Zhang1, Haizhen Wang1, E. Erdun3
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1515-1528, 2022, DOI:10.32604/cmc.2022.024779
Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of… More >
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