Welcome to an exciting journey into deep learning for image enhancement! This course, "Low-Light Image Enhancement and Deep Learning with Python," is designed to introduce you to advanced techniques and practical applications of deep learning using Python, Keras, and TensorFlow. Through a mix of hands-on projects and insightful lectures, you'll learn how to improve low-light images, reduce noise, and enhance image clarity using cutting-edge deep learning models.
Throughout the course, you'll master essential skills such as image data preprocessing and augmentation, and you'll gain proficiency in using Keras and TensorFlow to build and train deep learning models. You'll also get to explore advanced topics like selective kernel feature fusion, spatial and channel attention mechanisms, and multi-scale residual blocks, all of which contribute to superior image enhancement. Additionally, the course makes extensive use of Google Colab, providing a seamless cloud-based environment for developing, training, and evaluating your models.
By the end of this course, "Low-Light Image Enhancement and Deep Learning with Python", you'll have the knowledge and practical experience needed to tackle complex image enhancement tasks with confidence. Whether you're aiming to work in computer vision, image processing, or machine learning, the skills you gain here will open doors to rewarding job opportunities.
Low-Light Image Enhancement and Deep Learning with Python Table of Contents:
- Introduction - 01:38
- About This Project - 01:06
- Applications - 03:54
- Job Opportunities - 04:25
- Why Python, Keras, and Google Colab? - 02:36
- Working Directory Set Up - 00:52
- Dataset - 02:50
- What is inside Code.ipynb? - 00:24
- Launch Code - 00:39
- Enable the GPU - 00:37
- Mount Google Drive in a Google Colab Notebook - 01:48
- Import Various Libraries - 02:29
- Set Random Seed and Define Image Size and Batch Size - 02:49
- Read and Preprocess an Image - 02:24
- Randomly Cropping Images - 01:24
- Loading and Preprocessing Image Data - 01:24
- Constructing a TensorFlow Dataset Pipeline - 02:00
- Defining File Paths for Training, Validation, and Test Datasets - 01:14
- Initialize Datasets for Training and Validation - 01:41
- Selective Integration of Multi-Scale Features - 02:05
- Dynamic Learning of Spatial Attention Weights - 02:20
- Create a Channel-Wise Attention Mechanism - 02:17
- Combine Channel-Wise and Spatial-Wise Attention Mechanisms - 02:16
- Perform Feature Extraction - 02:05
- Increase the Spatial Dimensions of the Feature Maps - 02:05
- Multi-Scale Residual Block - 02:17
- Recursive Residual Group - 02:17
- Architecture for the Multiple Iterative Residual Network Model - 02:44
- Custom Loss and Evaluation Metric - 02:24
- Compiling - 01:23
- Training of the Model - 02:45
- Saving the Trained Model - 01:09
- Plotting the Training and Validation Loss - 02:22
- Plotting the Training and Validation Peak Signal-to-Noise Ratio - 01:07
- Visualize Multiple Images - 02:15
- Image Enhancement Using a Pre-Trained Model - 02:09
- Visual Inspection - 02:44
Who is this course for?
- Aspiring Programmers: Anyone interested in learning Python programming for image enhancement and low-light photography.
- Students: Those studying computer science, data science, or related fields with a focus on image processing and computer vision.
- Professionals: Individuals looking to enhance their skills in image enhancement techniques, especially for low-light photography.
- Hobbyists and Enthusiasts: Photography lovers are keen on exploring methods to improve image quality in difficult lighting conditions.
Click on the links below to Download Low-Light Image Enhancement and Deep Learning with Python!
Welcome to an exciting journey into deep learning for image enhancement! This course, "Low-Light Image Enhancement and Deep Learning with Python," is designed to introduce you to advanced techniques and practical applications of deep learning using Python, Keras, and TensorFlow. Through a mix of hands-on projects and insightful lectures, you'll learn how to improve low-light images, reduce noise, and enhance image clarity using cutting-edge deep learning models.
Throughout the course, you'll master essential skills such as image data preprocessing and augmentation, and you'll gain proficiency in using Keras and TensorFlow to build and train deep learning models. You'll also get to explore advanced topics like selective kernel feature fusion, spatial and channel attention mechanisms, and multi-scale residual blocks, all of which contribute to superior image enhancement. Additionally, the course makes extensive use of Google Colab, providing a seamless cloud-based environment for developing, training, and evaluating your models.
By the end of this course, "Low-Light Image Enhancement and Deep Learning with Python", you'll have the knowledge and practical experience needed to tackle complex image enhancement tasks with confidence. Whether you're aiming to work in computer vision, image processing, or machine learning, the skills you gain here will open doors to rewarding job opportunities.
Low-Light Image Enhancement and Deep Learning with Python Table of Contents:
- Introduction - 01:38
- About This Project - 01:06
- Applications - 03:54
- Job Opportunities - 04:25
- Why Python, Keras, and Google Colab? - 02:36
- Working Directory Set Up - 00:52
- Dataset - 02:50
- What is inside Code.ipynb? - 00:24
- Launch Code - 00:39
- Enable the GPU - 00:37
- Mount Google Drive in a Google Colab Notebook - 01:48
- Import Various Libraries - 02:29
- Set Random Seed and Define Image Size and Batch Size - 02:49
- Read and Preprocess an Image - 02:24
- Randomly Cropping Images - 01:24
- Loading and Preprocessing Image Data - 01:24
- Constructing a TensorFlow Dataset Pipeline - 02:00
- Defining File Paths for Training, Validation, and Test Datasets - 01:14
- Initialize Datasets for Training and Validation - 01:41
- Selective Integration of Multi-Scale Features - 02:05
- Dynamic Learning of Spatial Attention Weights - 02:20
- Create a Channel-Wise Attention Mechanism - 02:17
- Combine Channel-Wise and Spatial-Wise Attention Mechanisms - 02:16
- Perform Feature Extraction - 02:05
- Increase the Spatial Dimensions of the Feature Maps - 02:05
- Multi-Scale Residual Block - 02:17
- Recursive Residual Group - 02:17
- Architecture for the Multiple Iterative Residual Network Model - 02:44
- Custom Loss and Evaluation Metric - 02:24
- Compiling - 01:23
- Training of the Model - 02:45
- Saving the Trained Model - 01:09
- Plotting the Training and Validation Loss - 02:22
- Plotting the Training and Validation Peak Signal-to-Noise Ratio - 01:07
- Visualize Multiple Images - 02:15
- Image Enhancement Using a Pre-Trained Model - 02:09
- Visual Inspection - 02:44
Who is this course for?
- Aspiring Programmers: Anyone interested in learning Python programming for image enhancement and low-light photography.
- Students: Those studying computer science, data science, or related fields with a focus on image processing and computer vision.
- Professionals: Individuals looking to enhance their skills in image enhancement techniques, especially for low-light photography.
- Hobbyists and Enthusiasts: Photography lovers are keen on exploring methods to improve image quality in difficult lighting conditions.
Click on the links below to Download Low-Light Image Enhancement and Deep Learning with Python!
Welcome to an exciting journey into deep learning for image enhancement! This course, "Low-Light Image Enhancement and Deep Learning with Python," is designed to introduce you to advanced techniques and practical applications of deep learning using Python, Keras, and TensorFlow. Through a mix of hands-on projects and insightful lectures, you'll learn how to improve low-light images, reduce noise, and enhance image clarity using cutting-edge deep learning models.
Throughout the course, you'll master essential skills such as image data preprocessing and augmentation, and you'll gain proficiency in using Keras and TensorFlow to build and train deep learning models. You'll also get to explore advanced topics like selective kernel feature fusion, spatial and channel attention mechanisms, and multi-scale residual blocks, all of which contribute to superior image enhancement. Additionally, the course makes extensive use of Google Colab, providing a seamless cloud-based environment for developing, training, and evaluating your models.
By the end of this course, "Low-Light Image Enhancement and Deep Learning with Python", you'll have the knowledge and practical experience needed to tackle complex image enhancement tasks with confidence. Whether you're aiming to work in computer vision, image processing, or machine learning, the skills you gain here will open doors to rewarding job opportunities.
Low-Light Image Enhancement and Deep Learning with Python Table of Contents:
- Introduction - 01:38
- About This Project - 01:06
- Applications - 03:54
- Job Opportunities - 04:25
- Why Python, Keras, and Google Colab? - 02:36
- Working Directory Set Up - 00:52
- Dataset - 02:50
- What is inside Code.ipynb? - 00:24
- Launch Code - 00:39
- Enable the GPU - 00:37
- Mount Google Drive in a Google Colab Notebook - 01:48
- Import Various Libraries - 02:29
- Set Random Seed and Define Image Size and Batch Size - 02:49
- Read and Preprocess an Image - 02:24
- Randomly Cropping Images - 01:24
- Loading and Preprocessing Image Data - 01:24
- Constructing a TensorFlow Dataset Pipeline - 02:00
- Defining File Paths for Training, Validation, and Test Datasets - 01:14
- Initialize Datasets for Training and Validation - 01:41
- Selective Integration of Multi-Scale Features - 02:05
- Dynamic Learning of Spatial Attention Weights - 02:20
- Create a Channel-Wise Attention Mechanism - 02:17
- Combine Channel-Wise and Spatial-Wise Attention Mechanisms - 02:16
- Perform Feature Extraction - 02:05
- Increase the Spatial Dimensions of the Feature Maps - 02:05
- Multi-Scale Residual Block - 02:17
- Recursive Residual Group - 02:17
- Architecture for the Multiple Iterative Residual Network Model - 02:44
- Custom Loss and Evaluation Metric - 02:24
- Compiling - 01:23
- Training of the Model - 02:45
- Saving the Trained Model - 01:09
- Plotting the Training and Validation Loss - 02:22
- Plotting the Training and Validation Peak Signal-to-Noise Ratio - 01:07
- Visualize Multiple Images - 02:15
- Image Enhancement Using a Pre-Trained Model - 02:09
- Visual Inspection - 02:44
Who is this course for?
- Aspiring Programmers: Anyone interested in learning Python programming for image enhancement and low-light photography.
- Students: Those studying computer science, data science, or related fields with a focus on image processing and computer vision.
- Professionals: Individuals looking to enhance their skills in image enhancement techniques, especially for low-light photography.
- Hobbyists and Enthusiasts: Photography lovers are keen on exploring methods to improve image quality in difficult lighting conditions.
Click on the links below to Download Low-Light Image Enhancement and Deep Learning with Python!
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