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Image Super-Resolution

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하위 태스크: 5
평가표: 69
보존된 평가 기록: 748

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.

코드

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

https://github.com/stella-von/LKDN

공식 코드

Large Kernel Distillation Network for Efficient Single Image Super-Resolution

https://github.com/stella-von/MAT

공식 코드

MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution

https://github.com/striveagain/megasr

공식 코드

MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-Resolution

https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw

공식 코드

Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network

https://github.com/sun1992/hpun

공식 코드

Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution

https://github.com/sunny2109/mobilesr-ntire2022

공식 코드

ShuffleMixer: An Efficient ConvNet for Image Super-Resolution

https://github.com/sunny2109/safmn

공식 코드

Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution

https://github.com/sunwj/CAR

공식 코드

Learned Image Downscaling for Upscaling using Content Adaptive Resampler

https://github.com/superkenvery/autolut

공식 코드

AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning

https://github.com/supratikbanerjee/SubPixel-BackProjection_SuperResolution

공식 코드

Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution

https://github.com/swamiviv/CLASP

공식 코드

Semantic uncertainty intervals for disentangled latent spaces

https://github.com/swz30/MPRNet

공식 코드

MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution

https://github.com/sxpro/rfsr

공식 코드

RFSR: Improving ISR Diffusion Models via Reward Feedback Learning

https://github.com/t2mhanh/LapSRN_TextImages_git

공식 코드

Deep Laplacian Pyramid Network for Text Images Super-Resolution

https://github.com/tamarott/SinGAN

공식 코드

SinGAN: Learning a Generative Model from a Single Natural Image

https://github.com/tanedam/sdm-car

공식 코드

SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

https://github.com/tencent-ailab/frequency_aug_vae_moesr

공식 코드

Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach

https://github.com/tencentarc/desra

공식 코드

DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models

https://github.com/teryzh/reconstruction-ngsim-trajectory-with-dmd-and-res_unet_plus

공식 코드

Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks

https://github.com/teryzh/roadside-lidar-vehicle-detection-and-tracking-background-subtraction

공식 코드

Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction

https://github.com/theodore-pku/theorem-1-verification-python

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Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer

https://github.com/therebellll/NegIoU-PosIoU-Miou

공식 코드

Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection

https://github.com/thunil/TecoGAN

공식 코드

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/thy960112/mdbn

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Multi-Depth Branch Network for Efficient Image Super-Resolution

https://github.com/tirer-lab/cm4ir

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Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)

https://github.com/tomchenshi/cst

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Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution

https://github.com/tomchenshi/hsrmamba

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HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution

https://github.com/tomtirer/IDBP-CNN-IA

공식 코드

Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning

https://github.com/toyotainfotech/stisr-tcdm

공식 코드

Scene Text Image Super-resolution based on Text-conditional Diffusion Models

https://github.com/tyshiwo/MemNet

공식 코드

MemNet: A Persistent Memory Network for Image Restoration

https://github.com/um-dsrg/Super-Resolution-Meta-Attention-Networks

공식 코드

Improving Super-Resolution Performance using Meta-Attention Layers

https://github.com/um-dsrg/rumpy

공식 코드

The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

https://github.com/umautobots/driving-in-the-matrix

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Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

https://github.com/usman1021/fusion

공식 코드

A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution

https://github.com/usman1021/lightweight

공식 코드

Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network

https://github.com/ustc-imcl/hst-for-compressed-image-sr

공식 코드

HST: Hierarchical Swin Transformer for Compressed Image Super-resolution

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.