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 981–990 of 3874 papers

TitleStatusHype
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
LSwinSR: UAV Imagery Super-Resolution based on Linear Swin TransformerCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Make-A-Video: Text-to-Video Generation without Text-Video DataCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMsCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
ML-SIM: A deep neural network for reconstruction of structured illumination microscopy imagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified