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 3831–3840 of 3874 papers

TitleStatusHype
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Guided Frequency Loss for Image Restoration—0
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution—0
Gull: A Generative Multifunctional Audio Codec—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified