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 3491–3500 of 3874 papers

TitleStatusHype
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks—0
Enhancing Image Resolution: A Simulation Study and Sensitivity Analysis of System Parameters for Resourcesat-3S/3SA—0
Task-driven real-world super-resolution of document scans—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified