DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection
Deep neural advancements have recently brought remarkable image synthesis performance to the field of image inpainting. The adaptation of generative adversarial networks (GAN) in particular has accelerated significant progress in high-quality image reconstruction. However, although many notable GAN-based networks have been proposed for image inpainting, still pixel artifacts or color inconsistency occur in synthesized images during the generation process, which are usually called fake textures. To reduce pixel inconsistency disorder resulted from fake textures, we introduce a GAN-based model using dynamic attention map (DAM-GAN). Our proposed DAM-GAN concentrates on detecting fake texture and products dynamic attention maps to diminish pixel inconsistency from the feature maps in the generator. Evaluation results on CelebA-HQ and Places2 datasets with other image inpainting approaches show the superiority of our network.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationImage InpaintingImage ReconstructionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
We tackle the problem of texture inpainting where the input images are textures with missing values along with masks that indicate the zones that should be generated. Many works have been done in image inpainting with th…
Image InpaintingMissing ValuesDelving Globally into Texture and Structure for Image Inpainting
Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the mas…
DecoderImage InpaintingTexture Transform Attention for Realistic Image Inpainting
Over the last few years, the performance of inpainting to fill missing regions has shown significant improvements by using deep neural networks. Most of inpainting work create a visually plausible structure and texture, …
DecoderImage InpaintingMulti-feature Co-learning for Image Inpainting
Image inpainting has achieved great advances by simultaneously leveraging image structure and texture features. However, due to lack of effective multi-feature fusion techniques, existing image inpainting methods still s…
Image InpaintingImage Inpainting Guided by Coherence Priors of Semantics and Textures
Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories remains challenging due to the obscure seman…
Image InpaintingSemantic Segmentation