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 1371–1380 of 3874 papers

TitleStatusHype
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction—0
EgoVSR: Towards High-Quality Egocentric Video Super-ResolutionCode1
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified