https://github.com/fitushar/image_pixel_recovery_with_lesso_regression
The objective of this mini-project is to Recover a full image from a small number of sampled pixels (compressed sensing). Although the primary goal of this project is to understand and explore the application of regularized. In the process of recovering image pixel using regularized regression, we will explore different concepts and their understanding as following: Understanding how regression can be applied in 2D image analysis domain. Understanding of the discrete cosine transforms (DCT) to define an image in a frequency domain. Explore the importance and application of cross validation in model tunning and hyper-parameter selections. Understanding the impact of applying filtering approach such as median filter on reconstructed image Finally, quantitively evaluating the quality of removed image.
https://github.com/fitushar/image_pixel_recovery_with_lesso_regression
comprssed-sensing image machine-learning optimization pixel-recovery regression-algorithms regression-analysis
Last synced: 4 months ago
JSON representation
The objective of this mini-project is to Recover a full image from a small number of sampled pixels (compressed sensing). Although the primary goal of this project is to understand and explore the application of regularized. In the process of recovering image pixel using regularized regression, we will explore different concepts and their understanding as following: Understanding how regression can be applied in 2D image analysis domain. Understanding of the discrete cosine transforms (DCT) to define an image in a frequency domain. Explore the importance and application of cross validation in model tunning and hyper-parameter selections. Understanding the impact of applying filtering approach such as median filter on reconstructed image Finally, quantitively evaluating the quality of removed image.
- Host: GitHub
- URL: https://github.com/fitushar/image_pixel_recovery_with_lesso_regression
- Owner: fitushar
- Created: 2022-03-06T20:14:51.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-06T20:25:07.000Z (over 3 years ago)
- Last Synced: 2025-06-15T02:03:28.735Z (4 months ago)
- Topics: comprssed-sensing, image, machine-learning, optimization, pixel-recovery, regression-algorithms, regression-analysis
- Language: Python
- Homepage:
- Size: 2.94 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Image Pixel Recovery with Lesso-regression
The objective of this mini-project is to Recover a full image from a small number of sampled pixels (compressed sensing). Although the primary goal of this project is to understand and explore the application of regularized. In the process of recovering image pixel using regularized regression, we will explore different concepts and their understanding as following: Understanding how regression can be applied in 2D image analysis domain. Understanding of the discrete cosine transforms (DCT) to define an image in a frequency domain. Explore the importance and application of cross validation in model tunning and hyper-parameter selections. Understanding the impact of applying filtering approach such as median filter on reconstructed image Finally, quantitively evaluating the quality of removed image.
