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 1971–1980 of 3874 papers

TitleStatusHype
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images—0
Diffusion-based Light Field Synthesis—0
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data—0
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
Diffusion Image Prior—0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—0
Diffusion Posterior Sampling is Computationally Intractable—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified