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 891–900 of 3874 papers

TitleStatusHype
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Activating Wider Areas in Image Super-ResolutionCode1
PFStorer: Personalized Face Restoration and Super-Resolution—0
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Efficient Diffusion Model for Image Restoration by Residual ShiftingCode5
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified