SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3191–3200 of 3874 papers

TitleStatusHype
An approach to image denoising using manifold approximation without clean images—0
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
VideoGigaGAN: Towards Detail-rich Video Super-Resolution—0
3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy—0
Show:102550
← PrevPage 320 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified