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 1841–1850 of 3874 papers

TitleStatusHype
Incorporating Degradation Estimation in Light Field Spatial Super-Resolution—0
Kernel Aware Resampler—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Context Reasoning Attention Network for Image Super-Resolution—0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified