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 471–480 of 3874 papers

TitleStatusHype
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-ResolutionCode1
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up TablesCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-ResolutionCode1
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
SVCNet: Scribble-based Video Colorization Network with Temporal AggregationCode1
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image RestorationCode1
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-ResolutionCode1
LSwinSR: UAV Imagery Super-Resolution based on Linear Swin TransformerCode1
Iterative Soft Shrinkage Learning for Efficient Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified