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Found 5 papers, 1 papers with code
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
Real-World Atmospheric Turbulence Correction via Domain Adaptation
Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface.
Atmospheric Turbulence Correction via Variational Deep Diffusion
We use the learned information to further condition the diffusion model.
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution
Our approach leverages the degradation model and proposes a new formulation of the Convolutional Neural Network (CNN) cascade model, where each network sub-module is constrained to solve a specific degradation: deblurring or upsampling.
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
