修补
计算机科学
块(置换群论)
图像(数学)
人工智能
编码(集合论)
过程(计算)
像素
树(集合论)
计算机视觉
机器学习
数学
数学分析
几何学
集合(抽象数据类型)
程序设计语言
操作系统
作者
Xin Ma,Xiaoqiang Zhou,Huaibo Huang,Gengyun Jia,Yaohui Wang,Xinyuan Chen,Cunjian Chen
标识
DOI:10.1016/j.eswa.2023.121148
摘要
While most image inpainting methods perform well on small image defects, they still struggle to deliver satisfactory results on large holes due to insufficient image guidance. To address this challenge, this paper proposes an uncertainty-aware adaptive feedback network (U2AFN), which incorporates an adaptive feedback mechanism to refine inpainting regions progressively. U2AFN predicts both an uncertainty map and an inpainting result simultaneously. During each iteration, the adaptive integration feedback block utilizes inpainting pixels with low uncertainty to guide the subsequent learning iteration. This process leads to a gradual reduction in uncertainty and produces more reliable inpainting outcomes. Our approach is extensively evaluated and compared on multiple datasets, demonstrating its superior performance over existing methods. The code is available at: https://codeocean.com/capsule/1901983/tree.
科研通智能强力驱动
Strongly Powered by AbleSci AI