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 941–950 of 3874 papers

TitleStatusHype
Single Image Super-Resolution via CNN Architectures and TV-TV MinimizationCode1
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
SNIPS: Solving Noisy Inverse Problems StochasticallyCode1
Solving Inverse Problems via Diffusion Optimal ControlCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation TransformerCode1
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image FusionCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified