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Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales
Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
A comprehensive review of past and present image inpainting methods
Images can be described as visual representations or likeness of something (person or
object) which can be reproduced or captured, eg a hand drawing, photographic material …
object) which can be reproduced or captured, eg a hand drawing, photographic material …
DNNAM: Image inpainting algorithm via deep neural networks and attention mechanism
Y Chen, R **a, 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 …
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 …
MFMAM: Image inpainting via multi-scale feature module with attention module
Y Chen, R **a, K Yang, K Zou - Computer Vision and Image Understanding, 2024 - Elsevier
Currently, most image inpainting algorithms based on deep learning cause information loss
when acquiring deep level features. It is not conducive to the image inpainting of texture …
when acquiring deep level features. It is not conducive to the image inpainting of texture …
Recurrent feature reasoning for image inpainting
Existing inpainting methods have achieved promising performance for recovering regular or
small image defects. However, filling in large continuous holes remains difficult due to the …
small image defects. However, filling in large continuous holes remains difficult due to the …
PSCC-Net: Progressive spatio-channel correlation network for image manipulation detection and localization
To defend against manipulation of image content, such as splicing, copy-move, and
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …
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 …
[HTML][HTML] DARGS: Image inpainting algorithm via deep attention residuals group and semantics
Y Chen, R **a, K Yang, K Zou - Journal of King Saud University-Computer …, 2023 - Elsevier
To solving the problems that the existing image inpainting methods lack authenticity, do not
deal with the information of missing and non-missing regions flexibly, and do not deal with …
deal with the information of missing and non-missing regions flexibly, and do not deal with …
Uni-paint: A unified framework for multimodal image inpainting with pretrained diffusion model
Recently, text-to-image denoising diffusion probabilistic models (DDPMs) have
demonstrated impressive image generation capabilities and have also been successfully …
demonstrated impressive image generation capabilities and have also been successfully …