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 2251–2260 of 3874 papers

TitleStatusHype
XCycles Backprojection Acoustic Super-Resolution—0
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches—0
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
Convolutional Sparse Coding for Image Super-Resolution—0
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop—0
Memory-efficient Learning for Large-scale Computational Imaging—0
Memory Efficient Patch-based Training for INR-based GANs—0
Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified