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 641–650 of 3874 papers

TitleStatusHype
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution—0
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution—0
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-ResolutionCode0
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution—0
Attention Beats Linear for Fast Implicit Neural Representation GenerationCode1
Efficient Multi-disparity Transformer for Light Field Image Super-resolution—0
ThermalNeRF: Thermal Radiance Fields—0
A New Dataset and Framework for Real-World Blurred Images Super-ResolutionCode1
Large Kernel Distillation Network for Efficient Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified