Image inpainting via conditional texture and structure dual generation
Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper interaction with image …
inpainting by introducing structure priors. Due to the lack of proper interaction with image …
Deep learning for image inpainting: A survey
Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …
processing. The main purpose of image inpainting is to produce visually plausible structure …
Incremental transformer structure enhanced image inpainting with masking positional encoding
Image inpainting has made significant advances in recent years. However, it is still
challenging to recover corrupted images with both vivid textures and reasonable structures …
challenging to recover corrupted images with both vivid textures and reasonable structures …
Generating diverse structure for image inpainting with hierarchical VQ-VAE
Given an incomplete image without additional constraint, image inpainting natively allows
for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting …
for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting …
Image inpainting with local and global refinement
Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …
A review of image inpainting methods based on deep learning
Image Inpainting is an age-old image processing problem, with people from different eras
attempting to solve it using various methods. Traditional image inpainting algorithms have …
attempting to solve it using various methods. Traditional image inpainting algorithms have …
DNNAM: Image inpainting algorithm via deep neural networks and attention mechanism
Y Chen, R Xia, K Yang, K Zou - Applied Soft Computing, 2024 - Elsevier
Most image inpainting algorithms have problems such as fuzzy images, texture distortion
and semantic inaccuracy, and the image inpainting effect is limited when processing photos …
and semantic inaccuracy, and the image inpainting effect is limited when processing photos …
Deep learning on image stitching with multi-viewpoint images: A survey
N Yan, Y Mei, L Xu, H Yu, B Sun, Z Wang… - Neural Processing …, 2023 - Springer
Multi-viewpoint image stitching aims to stitch images taken from different viewpoints into
pictures with a broader field of view. The stitched images are subject to artifacts, geometric …
pictures with a broader field of view. The stitched images are subject to artifacts, geometric …
Image 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 …
images of specific scenes. However, filling holes involving multiple semantic categories …
Keys to better image inpainting: Structure and texture go hand in hand
Deep image inpainting has made impressive progress with recent advances in image
generation and processing algorithms. We claim that the performance of inpainting …
generation and processing algorithms. We claim that the performance of inpainting …