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 2276–2300 of 3874 papers

TitleStatusHype
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
Mid-wave infrared super-resolution imaging based on compressive calibration and sampling—0
Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study—0
Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
Continual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems—0
A Comprehensive Survey of Transformers for Computer Vision—0
Transformers in Vision: A Survey—0
MIRE: Matched Implicit Neural Representations—0
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging—0
Context Reasoning Attention Network for Image Super-Resolution—0
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
A comprehensive review on Plant Leaf Disease detection using Deep learning—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks—0
Translation-based Video-to-Video Synthesis—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified