Low-Light Image Enhancement and Deep Learning with Python

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Karthik K

1:14:58

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1. Fundamentals

1. Introduction.mp4

01:38

2. About this Project.mp4

01:06

3. Applications.mp4

03:54

4. Job Opportunities.mp4

04:25

5. Why Python, Keras, and Google Colab.mp4

02:36

2. Building and Training the Model

1.1 image-enhancement.rar

1. Working directory set up.mp4

00:52

2. Dataset.mp4

02:50

3. What is inside Code.ipynb.mp4

00:24

4. Launch Code.mp4

00:39

5. Enable the GPU.mp4

00:37

6. Mount Google Drive in a Google Colab notebook.mp4

01:48

7. Import various libraries.mp4

02:29

8. Sets random seed and defines image size and batch size.mp4

02:49

9. Read and preprocess an image.mp4

02:24

10. Randomly cropping images.mp4

01:24

11. Loading and preprocessing image data.mp4

01:24

12. Constructing a TensorFlow dataset pipeline.mp4

02:00

13. Defining file paths for training, validation, and test datasets.mp4

01:14

14. Initializes datasets for training and validation.mp4

01:41

15. Selectively integrate multi-scale features.mp4

02:05

16. Dynamically learn spatial attention weights.mp4

02:20

17. Create a channel-wise attention mechanism.mp4

02:17

18. Combines both channel-wise and spatial-wise attention mechanisms.mp4

02:16

19. Perform feature extraction.mp4

02:05

20. Increase the spatial dimensions of the feature maps.mp4

02:05

21. Multi-scale residual block.mp4

02:17

22. Recursive residual group.mp4

02:17

23. Architecture for the Multiple Iterative Residual Network model.mp4

02:44

24. Custom loss and evaluation metric.mp4

02:24

25. Compiling.mp4

01:23

26. Training of the model.mp4

02:45

27. Saving the trained model.mp4

01:09

28. Plotting the training and validation loss.mp4

02:22

29. Plotting the training and validation Peak Signal-to-Noise Ratio.mp4

01:07

30. Visualize multiple images.mp4

02:15

31. Image enhancement using a pre-trained model.mp4

02:09

32. Visual inspection.mp4

02:44

Description

Elevating Low-Light Photography with Python, Keras, Tensorflow, and Google Colab: A Deep Learning Hands-on Approach

What You'll Learn?

Understand the challenges faced in low-light photography and the importance of image enhancement techniques.
Gain familiarity with The LoL Dataset and its role as a resource for developing and evaluating low-light image enhancement algorithms.
Learn how to set up a working directory in Google Drive for organizing project files and datasets.
Acquire knowledge about the structure and contents of The LoL Dataset, including the training, testing, and validation sets.
Develop proficiency in using Python, Keras, and Google Colab for implementing low-light image enhancement algorithms.
Explore techniques, including selective kernel feature fusion, spatial and channel attention blocks, multi-scale residual blocks, and recursive residual groups.
Understand the concepts of custom loss functions and metrics for evaluating model performance in image enhancement tasks.
Gain practical experience in training, evaluating, and fine-tuning deep learning models for low-light image enhancement using real-world datasets.
Learn how to visualize and analyze model training progress, including loss and performance metrics over epochs.
Develop the skills to deploy trained models for enhancing low-light images and generating visually appealing results.

Who is this for?

Individuals interested in learning Python programming for image enhancement and low-light photography.

Students pursuing studies in computer science, data science, or related fields with a focus on image processing and computer vision.

Professionals seeking to enhance their skills in image enhancement techniques, particularly in the context of low-light photography.

Hobbyists and enthusiasts passionate about photography and interested in exploring techniques to improve image quality in challenging lighting conditions.

What You Need to Know?

Access to a computer with a stable internet connection.

A Google account for accessing Google Colab and Google Drive, where the course materials and datasets are hosted.

More details

Description
Welcome to the immersive world of deep learning for image enhancement! In this comprehensive course, students will delve into cutting-edge techniques and practical applications of deep learning using Python, Keras, and TensorFlow. Through hands-on projects and theoretical lectures, participants will learn how to enhance low-light images, reduce noise, and improve image clarity using state-of-the-art deep learning models.

Key Learning Objectives:
Understand the fundamentals of deep learning and its applications in image enhancement.
Explore practical techniques for preprocessing and augmenting image data using Python libraries.
Implement deep learning models for image enhancement tasks.
Master the use of Keras and TensorFlow frameworks for building and training deep learning models.
Utilize Google Colab for seamless development, training, and evaluation of deep learning models in a cloud-based environment.
Gain insights into advanced concepts such as selective kernel feature fusion, spatial and channel attention mechanisms, and multi-scale residual blocks for superior image enhancement results.
Apply learned techniques to real-world scenarios and datasets, honing practical skills through hands-on projects and assignments.
Prepare for lucrative job opportunities in fields such as computer vision, image processing, and machine learning, equipped with the practical skills and knowledge gained from the course.

By the end of this course, students will have the expertise to tackle complex image enhancement tasks using deep learning techniques and tools. Armed with practical experience and theoretical understanding, graduates will be well-positioned to secure rewarding job opportunities in industries seeking expertise in image processing and deep learning technologies.
Who this course is for:
Individuals interested in learning Python programming for image enhancement and low-light photography.
Students pursuing studies in computer science, data science, or related fields with a focus on image processing and computer vision.
Professionals seeking to enhance their skills in image enhancement techniques, particularly in the context of low-light photography.
Hobbyists and enthusiasts passionate about photography and interested in exploring techniques to improve image quality in challenging lighting conditions.

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Engineer dedicated to utilizing the power of Machine learning and Deep learning to solve real-world problems, improve design and performance assessment. Over ten years of experience in engineering and R&D environment. Engineering professional with a focus on Multi-physics CFD-ML from IIT Madras. Experienced in implementing action-oriented solutions to complex business problem.

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