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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1276–1300 of 3874 papers

TitleStatusHype
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting—0
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolutionCode1
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution—0
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual ShiftingCode3
On the Effectiveness of Spectral Discriminators for Perceptual Quality ImprovementCode1
Real-Time Neural Video Recovery and Enhancement on Mobile Devices—0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network—0
Towards Robust Scene Text Image Super-resolution via Explicit Location EnhancementCode1
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
A comparative analysis of SRGAN models—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-ResolutionCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-ResolutionCode1
MaxSR: Image Super-Resolution Using Improved MaxViT—0
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework—0
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational ImagingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified