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 2931–2940 of 3874 papers

TitleStatusHype
A statistically constrained internal method for single image super-resolution—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
RGB-Guided Resolution Enhancement of IR Images—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified