jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022
This project helps improve the clarity and detail of low-resolution images by generating higher-resolution versions. You input a blurry or pixelated image, and it outputs a sharpened, more detailed image. It's designed for researchers and engineers working in computer vision or image processing who need to upscale images for various applications.
194 stars. No commits in the last 6 months.
Use this if you need to perform high-quality image super-resolution, enhancing fine textures and details in digital images.
Not ideal if you are a casual user looking for a simple, point-and-click photo enhancer, as it requires technical setup and command-line operations.
Stars
194
Forks
32
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Oct 20, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jaewon-lee-b/lte"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
cabooster/DeepCAD-RT
DeepCAD-RT: Real-time denoising of fluorescence time-lapse imaging using deep self-supervised learning