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

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Showing 2011–2020 of 3874 papers

TitleStatusHype
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework—0
MaxSR: Image Super-Resolution Using Improved MaxViT—0
DWA: Differential Wavelet Amplifier for Image Super-ResolutionCode0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets—0
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution—0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution—0
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified