计算机应用研究2024，Vol.41Issue(6)：1893-1900,8.DOI:10.19734/j.issn.1001-3695.2023.09.0406
基于优化感受野策略的图像修复方法
Deep neural network inpainting method based on optimized receptive field strategy
摘要
Abstract
The currently popular image inpainting methods based on deep neural network typically employ large receptive field feature extractors.However,when restoring local patterns and textures,they often generate artifacts or distorted textures,thus failing to recover the overall semantic and visual structure of the image.To address this issue,this paper proposed a novel image inpainting method,called ORFNet,which combined coarse and fine inpainting by employing an optimized receptive field strategy.Initially,it obtained a coarse inpainting result by using a generative adversarial network with a large receptive field.Subsequently,it used a model with a small receptive field to refine local texture details.Finally,it performed a global refinement inpainting by using an encoder-decoder network based on attention mechanisms.Validation on the CelebA,Paris StreetView,and Places2 datasets demonstrates that ORFNet outperforms existing representative inpainting methods.It leads to 1.98 dB increase in PSNR and 2.49％improvement in SSIM,along with average 2.4％reduction in LPIPS.Experimental re-sults confirm the effectiveness of the proposed image inpainting method,showcasing superior performance across various recep-tive field settings and achieving more realistic and natural visual outcome.关键词
自编码网络/语义一致/感受野/注意力/粗修复和细修复Key words
autoencoder network/semantic consistency/receptive field/attention/coarse-fine inpainting分类
信息技术与安全科学引用本文复制引用
刘恩泽,刘华明,王秀友,毕学慧..基于优化感受野策略的图像修复方法[J].计算机应用研究,2024,41(6):1893-1900,8.基金项目
安徽省高校自然科学研究重大项目(KJ2020ZD46) （KJ2020ZD46）
阜阳师范大学高层次人才科研启动项目(2020KYQD0032) （2020KYQD0032）
阜阳师范大学校级项目(rcxm202001,2020FSKJ12,2021FSKJ01ZD) （rcxm202001,2020FSKJ12,2021FSKJ01ZD）
阜阳市校合作项目(SXHZ202103) （SXHZ202103）
阜阳师范大学阜阳市产业链研究创新团队(CYLTD202213) （CYLTD202213）
