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 531–540 of 3874 papers

TitleStatusHype
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive ConvolutionCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-ResolutionCode1
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse ArraysCode1
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified