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
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
Image Neural Field Diffusion Models—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified