This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar dat
Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently. However, as low-resolution (LR) images often undergo severe degradation, it is challenging for ISR models to perceive the semantic and degradation information...
Diffusion model for Low-Light Enchancement Diffusion model for other tasks Benchmark Datasets Diffusion model for Image/video compression Diffusion model for Image/video quality assessment Image Super-Resolution ModelsPaperFirst AuthorTraining WayVenueTopicProject SR3 Image super-resolution via iterative re...
This includes deterministic models such as Enhanced Deep Residual Networks for Super-Resolution with Generative Adversarial framework (EDSR-GAN), known for their superiority over traditional statistical techniques40,41,42,43, as well as the widely-used linear interpolation methods (Lerp) in meteorology...
Low-light image enhancement is pivotal for augmenting the utility and recognition of visuals captured under inadequate lighting conditions. Previous methods based on Generative Adversarial Networks (GAN) are affected by mode collapse and lack attention to the inherent characteristics of low-light images....
The diffusion probabilistic model (DPM) has achieved unparalleled results in current image generation tasks, and some recent research works employed it in several computer vision tasks, such as image super-resolution, object detection, etc. Thanks to DPM's superior ability to generate fine-grained ...
Parallel and efficient approximate nearest patch matching for image editing applications Neurocomputing (2018) H. Huang et al. Stfdiff: Remote sensing image spatiotemporal fusion with diffusion models Inf. Fusion (2024) H. Li et al. Srdiff: Single image super-resolution with diffusion probabilistic...
The accurate latent codes are composed as the starting noise for the diffusion process. Through the gradual injection of composite self-attention maps that are specifically designed to reflect the relations between guid- ing images, we are able to infuse contextual info...
报告嘉宾:曹杰彰 (Harvard University) 报告时间:2024年12月25日 (星期三)晚上20:40 (北京时间) 报告题目:Diffusion Model-based Restoration: Sequential Sampling vs Parallel Sampling 报告人简介: Jiezhang Cao is currently a postdoctoral researcher in the Department of Psychiatry at Harvard Medical School, ...
Recovering degraded low-resolution text images is challenging, especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently, diffusion models have ac...