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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
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
Attention-based Multi-Reference Learning for Image Super-Resolution
Transformer for Single Image Super-Resolution
Multi-Attributed and Structured Text-to-Face Synthesis
SwinIR: Image Restoration Using Swin Transformer
edge-SR: Super-Resolution For The Masses
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
spectrai: A deep learning framework for spectral data
Light Field Image Super-Resolution with Transformers
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution
Discovering Distinctive "Semantics" in Super-Resolution Networks
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN
LAConv: Local Adaptive Convolution for Image Fusion
Provident Vehicle Detection at Night for Advanced Driver Assistance Systems
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution
Image restoration quality assessment based on regional differential information entropy
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment
Blind Image Super-Resolution: A Survey and Beyond
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
Impact of deep learning-based image super-resolution on binary signal detection
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
Blind Image Super-Resolution via Contrastive Representation Learning
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