本次分享的是Google在text-to-image方面的论文:Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding,简称Imagen。 模型结构 Text Encoder:预训练好的文本编码器 Text-to-Image Diffusion Model:通过扩散模型,实现文本到低分辨率图像的生成 Super-Resolution Diffusion Model:将低分辨率图像进行两...
[论文阅读笔记]ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution 麦艾斯 允许自己做自己,允许别人做别人11 人赞同了该文章 1. 目标问题 图像超分任务中,输入图像的退化会导致图像中的高频细节丢失,并且由于退化手段的多样性,多个HR图像可能产生相同的LR图像,目前diffusion的图像生成能力有...
Current super-resolution (SR) methods often suffer from high-frequency texture distortion, excessive smoothing, and scale inconsistency. This study introduces an innovative implicit residual diffusion model (ImpRes) to address these issues. ImpRes enhances model convergence speed and high-frequency detail ...
Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are ...
提出了Latent Diffusion Models(LDMs) 1、对比transformer-based的方法,该方法能够在压缩的空间(work on a compression level)对图像进行重建,生成比之前的方法更加可靠与详细的结果。并能应用于百万像素图像的高分辨率合成(high-resolution synthesis of megapixel images)。
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsHaoying Li 1 , Yifan Yang 1 , Meng Chang 1 , Huajun Feng 1∗ , Zhihai Xu 1 , Qi Li 1 and YuetingChen 11 Zhejiang University{lhaoying,yangyifan,changm,fenghj,xuzhi,liqi,chenyt}@zju.edu.cnAbstractSingle image supe...
内容提示: SRDiff: Single image super-resolution with diffusion probabilisticmodelsHaoying Lia , Yifan Yang a , Meng Chang a , Shiqi Chen a , Huajun Feng a, ⇑ , Zhihai Xu a , Qi Li a , Yueting Chen aa State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou...
Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image corresponds to multiple HR images. Recently, learning-based SISR methods have greatly outperformed traditional ones, whi...
This cascade approach involves chaining together multiple generative models over several spatial resolutions: one diffusion model that generates data at a low resolution, followed by a sequence of SR3 super-resolution diffusion models that gradually increase the resolution of the generated image to the ...
提出了Latent Diffusion Models(LDMs) 1、对比transformer-based的方法,该方法能够在压缩的空间(work on a compression level)对图像进行重建,生成比之前的方法更加可靠与详细的结果。并能应用于百万像素图像的高分辨率合成(high-resolution synthesis of megapixel images)。