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 1951–1975 of 3874 papers

TitleStatusHype
Kernel Adversarial Learning for Real-world Image Super-resolution—0
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing—0
Kernel Aware Resampler—0
Kernel based low-rank sparse model for single image super-resolution—0
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
When to Use Convolutional Neural Networks for Inverse Problems—0
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data—0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Photo Cropper and Enhancer—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Learning to Learn to Compress—0
L^2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Label-free Super-Resolution Microvessel Color Flow Imaging with Ultrasound—0
Label super-resolution networks—0
Label Super Resolution with Inter-Instance Loss—0
LAConv: Local Adaptive Convolution for Image Fusion—0
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified