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 376–400 of 3874 papers

TitleStatusHype
Deep Blind Super-Resolution for Satellite VideoCode1
DSR: Towards Drone Image Super-ResolutionCode1
Deep Audio Waveform PriorCode1
A Tree-guided CNN for image super-resolutionCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRICode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Attention in Attention Network for Image Super-ResolutionCode1
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
Attentive Fine-Grained Structured Sparsity for Image RestorationCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
A Two-Stage Attentive Network for Single Image Super-ResolutionCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Deep Blind Video Super-resolutionCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Deep Image PriorCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified