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 1381–1390 of 3874 papers

TitleStatusHype
Fast Samplers for Inverse Problems in Iterative Refinement ModelsCode0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
Combining Contrastive and Supervised Learning for Video Super-Resolution DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified