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 1081–1090 of 3874 papers

TitleStatusHype
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
Multi-Objective Reinforced Evolution in Mobile Neural Architecture SearchCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth CompletionCode0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified