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 11–20 of 3874 papers

TitleStatusHype
GLEAN: Generative Latent Bank for Image Super-Resolution and BeyondCode5
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA ApproachCode4
Adversarial Diffusion Compression for Real-World Image Super-ResolutionCode4
One-Step Effective Diffusion Network for Real-World Image Super-ResolutionCode4
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image GenerationCode4
A Survey on Visual MambaCode4
SeeSR: Towards Semantics-Aware Real-World Image Super-ResolutionCode4
DiffBIR: Towards Blind Image Restoration with Generative Diffusion PriorCode4
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space ModelCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified