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Super-Resolution

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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/arsenalcheng/mma

Activating Wider Areas in Image Super-Resolution

https://github.com/artem-gorodetskii/wikiart-latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/arthur-qiu/freenoise-lavie

LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

https://github.com/aryan-mann/style-transfer

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/ashantanu/DCGAN

Generative Adversarial Networks

https://github.com/ashawkey/jiif

Joint Implicit Image Function for Guided Depth Super-Resolution

https://github.com/ashishmurali/Generative-Adversarial-Network

Generative Adversarial Networks

https://github.com/ashkanpakzad/atn

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/ashukid/Conditional-GAN-pytorch

Generative Adversarial Networks

https://github.com/asiltureli/gan-in-colab

Generative Adversarial Networks

https://github.com/asraman9792/Image-Dehazing-GANS

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/assafshocher/ZSSR

"Zero-Shot" Super-Resolution using Deep Internal Learning

https://github.com/astro-informatics/rcGAN

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems

https://github.com/atheesh1998/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/atriumlts/subpixel

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/avillemin/GANs

Generative Adversarial Networks

https://github.com/ayanglab/swinganmr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/ayanglab/swinmr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/aynetdia/mtp_curriculum

Blockwise Parallel Decoding for Deep Autoregressive Models

https://github.com/ayushmankumar7/SISR-Tensorflow-2.x

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/ayushnawal/Channel-Estimation

Deep Learning-Based Channel Estimation

https://github.com/azzarelli/wape

WIRE: Wavelet Implicit Neural Representations

https://github.com/b5071/pulse-rev

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/baaivision/vid2vid-zero

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/balbasty/super-resolution

MRI Super-Resolution using Multi-Channel Total Variation

https://github.com/baowenbo/MEMC-Net

MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

https://github.com/bardank/dc-gan

Generative Adversarial Networks

https://github.com/basile-bron/Artificial-Intelligence-Bible

Generative Adversarial Networks

https://github.com/basile-bron/Artificial-Intelligence-Lexical

Generative Adversarial Networks

https://github.com/basilevh/image-outpainting

SinGAN: Learning a Generative Model from a Single Natural Image

https://github.com/bayuudachi/Real-esr-gan

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

https://github.com/bdehaine/SinGan

SinGAN: Learning a Generative Model from a Single Natural Image

https://github.com/behnoudshafizadeh/ESPCN-SuperResolution-Tensorflow

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/bencottier/GANCS

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/benearnthof/fm_boosting

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/benjs/DCTransformer-PyTorch

Generating Images with Sparse Representations

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