Pixel-wise Dense Detector for Image Inpainting
Ruisong Zhang, Weize Quan, Baoyuan Wu, Zhifeng Li, Dong-Ming Yan

TL;DR
This paper introduces a pixel-wise dense detector for image inpainting that improves artifact localization and automatically balances loss functions, leading to superior inpainting results.
Contribution
It proposes a novel detection-based framework with pixel-wise artifact localization and automatic loss balancing for improved image inpainting.
Findings
Outperforms existing methods on multiple datasets
Effectively localizes artifacts at pixel level
Automatically balances adversarial and reconstruction losses
Abstract
Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts. Moreover, the adversarial loss and reconstruction loss (e.g., l1 loss) are combined with tradeoff weights, which are also difficult to tune. In this paper, we propose a novel detection-based generative framework for image inpainting, which adopts the min-max strategy in an adversarial process. The generator follows an encoder-decoder architecture to fill the missing regions, and the detector using weakly supervised learning localizes the position of artifacts in a pixel-wise manner. Such position information makes the generator pay attention to artifacts and further enhance them. More importantly, we explicitly insert the output of the detector into the reconstruction loss with a weighting…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques · Digital Media Forensic Detection
MethodsInpainting
