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 3621–3630 of 3874 papers

TitleStatusHype
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Deep Back-Projection Networks For Super-ResolutionCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural NetworkCode0
Deep Back-Projection Networks for Single Image Super-resolutionCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
Image Super-resolution via Feature-augmented Random ForestCode0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified