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 2776–2800 of 3874 papers

TitleStatusHype
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
Beyond MR Image Harmonization: Resolution Matters Too—0
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network—0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Realistic Hair Synthesis with Generative Adversarial Networks—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Real-time, low-cost multi-person 3D pose estimation—0
Real-Time Neural-Enhancement for Online Cloud Gaming—0
Real-Time Neural Video Recovery and Enhancement on Mobile Devices—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified