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 901–910 of 3874 papers

TitleStatusHype
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Journey Towards Tiny Perceptual Super-ResolutionCode1
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastCode1
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-ResolutionCode1
Lightweight super resolution network for point cloud geometry compressionCode1
Beyond the Spectrum: Detecting Deepfakes via Re-SynthesisCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-ResolutionCode1
Show:102550
← PrevPage 91 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified