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 1061–1070 of 3874 papers

TitleStatusHype
Joint Multiple FMCW Chirp Sequence Processing for Velocity Estimation and Ambiguity Resolving—0
Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Spatial-Temporal Contrasting for Fine-Grained Urban Flow InferenceCode1
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolutionCode1
Infrared Image Super-Resolution via GAN—0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified