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Super-Resolution

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하위 태스크: 7
평가표: 1
보존된 평가 기록: 1

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.

<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dasongli1/learning_degradation

공식 코드

Learning Degradation Representations for Image Deblurring

https://github.com/daviddmc/STRESS

공식 코드

STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning

https://github.com/davide-coccomini/adversarial-magnification-to-deceive-deepfake-detection-through-super-resolution

공식 코드

Adversarial Magnification to Deceive Deepfake Detection through Super Resolution

https://github.com/davidxujiayang/cpnets

공식 코드

Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems

https://github.com/dawnhh/dlsr-pytorch

공식 코드

Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution

https://github.com/dberga/iquaflow-qmr-eo

공식 코드

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

https://github.com/dberga/iquaflow-qmr-loss

공식 코드

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

https://github.com/dbseorms16/haze_net

공식 코드

HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images

https://github.com/ddlee-cn/mulut

공식 코드

Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables

https://github.com/deep-imagelab/hprn

공식 코드

HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution

https://github.com/dengzeshuai/srtta

공식 코드

Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

https://github.com/destiny301/ssr

공식 코드

Transformer-based Selective Super-Resolution for Efficient Image Refinement

https://github.com/devzhk/PINO

공식 코드

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/diegovalsesia/deepsum

공식 코드

DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images

https://github.com/diegovalsesia/piunet

공식 코드

Permutation invariance and uncertainty in multitemporal image super-resolution

https://github.com/diff7/QuanToaster

공식 코드

QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise

https://github.com/dingsheng-ong/ipr-gan

공식 코드

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack

https://github.com/dlej/titan-implicit-prior

공식 코드

TITAN: Bringing The Deep Image Prior to Implicit Representations

https://github.com/dmhdmhdmh/liwt

공식 코드

Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

https://github.com/dmlc/xgboost

공식 코드

XGBoost: A Scalable Tree Boosting System

https://github.com/dongheehand/unsplash2K

공식 코드

Noise Conditional Flow Model for Learning the Super-Resolution Space

https://github.com/dongrunmin/refdiff

공식 코드

Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model

https://github.com/donkeymouse/thermalchameleon

공식 코드

Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images

https://github.com/dootmaan/pfseg-full

공식 코드

Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

https://github.com/dribnet/plat

공식 코드

Sampling Generative Networks

https://github.com/dslisleedh/ESC

공식 코드

Emulating Self-attention with Convolution for Efficient Image Super-Resolution

https://github.com/dslisleedh/IGConv

공식 코드

Implicit Grid Convolution for Multi-Scale Image Super-Resolution

https://github.com/dslisleedh/PLKSR

공식 코드

Partial Large Kernel CNNs for Efficient Super-Resolution

https://github.com/dsshim0125/FS-NCSR

공식 코드

FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow

https://github.com/dsy199610/flow-enhancer-sr-mrsi

공식 코드

Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging

https://github.com/duanlab123/curvpnp

공식 코드

CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser

https://github.com/dusongcheng/mossfuse

공식 코드

Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality Decoupling

https://github.com/dvlab-research/MASA-SR

공식 코드

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution

https://github.com/dvlab-research/Simple-SR

공식 코드

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond

https://github.com/dydjw9/RAIN

공식 코드

RAIN: A Simple Approach for Robust and Accurate Image Classification Networks

https://github.com/dzld00/pytorch-manifold-matching

공식 코드

Manifold Matching via Deep Metric Learning for Generative Modeling

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