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 3076–3100 of 3874 papers

TitleStatusHype
Online Streaming Video Super-Resolution with Convolutional Look-Up Table—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
On the modern deep learning approaches for precipitation downscaling—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging—0
On training deep networks for satellite image super-resolution—0
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
Optical Flow for Video Super-Resolution: A Survey—0
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Optimal Physical Preprocessing for Example-Based Super-Resolution—0
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism—0
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications—0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified