Uncertainty-Aware Image Inpainting with Adaptive Feedback Network


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.

Expert Systems with Applications
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Xin Ma
Xin Ma

I’m a Ph.D canditate at Monash University. My research interests include image super-resolution and inpainting, model compression, face recognition, video generation, large-scale generative models, etc