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 1501–1510 of 3874 papers

TitleStatusHype
RAIN: A Simple Approach for Robust and Accurate Image Classification NetworksCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
Image Super-Resolution via Attention based Back Projection NetworksCode0
Image Super-Resolution via Deep Recursive Residual NetworkCode0
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified