Search Results for author:
Found 9 papers, 6 papers with code
HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement
In practical applications, conventional methods generate large volumes of low-light images that require compression for efficient storage and transmission.
Visual Autoregressive Modeling for Image Super-Resolution
Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models.
Plug-and-Play Tri-Branch Invertible Block for Image Rescaling
High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details.
Ranked #1 on
Image Rescaling
on DIV2K val-q30-2x
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Specifically, the predictor estimates pixel offsets between the first and second compression, which are then utilized to divide different patterns.
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
In this paper, we propose a VQA method named PTM-VQA, which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks, enabling benefits for VQA from different aspects.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
CasSR: Activating Image Power for Real-World Image Super-Resolution
In particular, we develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images.
CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement
Specifically, the ITA module aggregates temporal information from consecutive frames and coding priors, while the MNA module globally captures spatial information guided by residual frames.
XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution
Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently.
