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 291–300 of 3874 papers

TitleStatusHype
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential EquationsCode1
SuperFormer: Volumetric Transformer Architectures for MRI Super-ResolutionCode1
ReLUs Are Sufficient for Learning Implicit Neural RepresentationsCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-ResolutionCode1
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion TeacherCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Exploring the Low-Pass Filtering Behavior in Image Super-ResolutionCode1
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion PriorCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified