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 3751–3775 of 3874 papers

TitleStatusHype
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
GenDR: Lightning Generative Detail Restorator—0
E-FCNN for tiny facial expression recognition—0
Generalizable One-shot Neural Head Avatar—0
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
Generating Unobserved Alternatives—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Generative adversarial network for super-resolution imaging through a fiber—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified