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 1981–1990 of 3874 papers

TitleStatusHype
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Directional diffusion models for graph representation learning—0
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks—0
DISCO: Distributed Inference with Sparse Communications—0
Disentangling Light Fields for Super-Resolution and Disparity Estimation—0
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Distilling with Residual Network for Single Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified