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 901–910 of 3874 papers

TitleStatusHype
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale UpsamplingCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image FusionCode1
Revisiting Temporal Alignment for Video RestorationCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Beyond the Spectrum: Detecting Deepfakes via Re-SynthesisCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified