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 1131–1140 of 3874 papers

TitleStatusHype
Redefining Neural Operators in d+1 Dimensions—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable WeightsCode0
Depth Anything with Any Prior—0
Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism—0
SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified