Contextual Residual Aggregation

2000년 도입 · 논문 1편에서 사용

Contextual Residual Aggregation, or CRA, is a module for image inpainting. It can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a low-resolution inpainted result and up-sample it to yield a large blurry image. Then we produce the high-frequency residuals for in-hole patches by aggregating weighted high-frequency residuals from contextual patches. Finally, we add the aggregated residuals to the large blurry image to obtain a sharp result.

출처: Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

소개 논문: Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting