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 1481–1490 of 3874 papers

TitleStatusHype
Improving Super-Resolution Methods via Incremental Residual LearningCode0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
Improved Pothole Detection Using YOLOv7 and ESRGANCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
FSRNet: End-to-End Learning Face Super-Resolution with Facial PriorsCode0
cGANs with Projection DiscriminatorCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
CFSNet: Toward a Controllable Feature Space for Image RestorationCode0
Image Super-resolution via Feature-augmented Random ForestCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified