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 1301–1310 of 3874 papers

TitleStatusHype
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement—0
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images—0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
A parametric non-negative coupled canonical polyadic decomposition algorithm for hyperspectral super-resolution—0
Binary Diffusion Probabilistic Model—0
Gradient-Free Adversarial Purification with Diffusion Models—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified