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 3631–3640 of 3874 papers

TitleStatusHype
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Feature-based Recognition Framework for Super-resolution Images—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
Feature-Driven Super-Resolution for Object Detection—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified