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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
논문
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ISTA-Inspired Network for Image Super-Resolution
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks
CUF: Continuous Upsampling Filters
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images
Deep Fourier Up-Sampling
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
Single Image Super-Resolution Based on Capsule Neural Networks
Accurate Image Restoration with Attention Retractable Transformer
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Multi-scale Attention Network for Single Image Super-Resolution
A heterogeneous group CNN for image super-resolution
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
Effective Invertible Arbitrary Image Rescaling
Face Super-Resolution Using Stochastic Differential Equations
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data
Thermal infrared image based vehicle detection in low-level illumination conditions using multi-level GANs
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach
Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation
Diffusion Models: A Comprehensive Survey of Methods and Applications
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
DSR: Towards Drone Image Super-Resolution
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars
Sliding Window Recurrent Network for Efficient Video Super-Resolution
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
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