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You will get guidance on how to come to Super-Resolution using Low-Res Imagery in Python

Lampros M.Status: Offline
Lampros M. Lampros M.
4.9
Top Rated
Preview imagePreview imagePreview imagePreview image

Let a pro handle the details

Buy Generative AI services from Lampros, priced and ready to go.
Lampros M.Status: Offline
Lampros M. Lampros M.
4.9
Top Rated
Preview imagePreview imagePreview imagePreview image

Let a pro handle the details

Buy Generative AI services from Lampros, priced and ready to go.

Project details

Nowadays, low-resolution imagery is abundant whereas high-resolution imagery is scarce. Being able to train a deep neural network to enhance the image resolution of low-resolution imagery will allow someone to take advantage of the high acquisition rates and this is the aim of this project.
Using Python, Pytorch, deep neural networks and open-source code this project allows users to train a combination of high- and low-resolution imagery to receive super-resolution images.
The price of this project is negotiable depending on the requirements.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, StyleGAN
AI Applications
Image Analysis, Image Processing, Image Upscaling
AI Development Language
Python
AI Tools
PyTorch
AI Models
BERT

What's included $450

These options are included with the project scope.

$450
  • Delivery Time 5 days
    • Image Upscaling
4.9
69 reviews
93% Complete
(64)
6% Complete
(4)
1% Complete
(1)
1% Complete
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1% Complete
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About Lampros

Lampros M.Status: Offline
R, Python, Remote Sensing, Deep Learning and Machine Learning Analyst
100% Job Success
4.9  (69 reviews)
Paramythia, Greece - 3:59 am local time
For more than a decade, I utilize the R and Python programming languages to process, visualize and extract information from data and for almost five years for Geospatial analysis. I'm the author / maintainer of R packages (I've submitted more than 10 to CRAN). You can view my Github profile at the following weblink: github.com/mlampros

I work with R and Rstudio on a daily basis. I can work with machine learning algorithms based on almost all CRAN, Github or Gitlab repositories. I can utilize visualization R packages such as ggplot2, plotly, tmap, leaflet, mapview. I can create shiny applications (shiny.rstudio.com/gallery/) and report my results in .pdf, word, .html or any other available format using Rmarkdown.

Moreover, I'm capable of using the hybrid 'Rcpp' and 'RcppArmadillo' R packages to improve the efficiency of R code and the Keras and Pytorch deep learning libraries for regression, classification, object detection or image segmentation (with or without pre-trained models).

Steps for completing your project

After purchasing the project, send requirements so Lampros can start the project.

Delivery time starts when Lampros receives requirements from you.

Lampros works on your project following the steps below.

Revisions may occur after the delivery date.

Downloading imagery or usage of existing ones

Images will be downloaded or an existing dataset will be used

Creating a super-resolution deep neural network

Using open source code a super-resolution deep neural network will be created and trained

Review the work, release payment, and leave feedback to Lampros.