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 3021–3030 of 3874 papers

TitleStatusHype
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion—0
Self-Enhanced Convolutional Network for Facial Video Hallucination—0
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel—0
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified