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Found 8 papers, 6 papers with code
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods.
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks.
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
However, the majority of available resources are still in standard dynamic range (SDR).
GET3D--: Learning GET3D from Unconstrained Image Collections
While generative models have shown potential in creating 3D textured shapes from 2D images, their applicability in 3D industries is limited due to the lack of a well-defined camera distribution in real-world scenarios, resulting in low-quality shapes.
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
In this work, we design an efficient SR network by improving the attention mechanism.
Blueprint Separable Residual Network for Efficient Image Super-Resolution
One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
