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 951–960 of 3874 papers

TitleStatusHype
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and LatencyCode1
Efficient scene text image super-resolution with semantic guidanceCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsCode1
SRDTI: Deep learning-based super-resolution for diffusion tensor MRICode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified