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
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
Fast and Memory-Efficient Network Towards Efficient Image Super-ResolutionCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference ModelingCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-ResolutionCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Neural Vocoder is All You Need for Speech Super-resolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified