Low-Light Image Enhancement and Deep Learning with Python

سرفصل های دوره

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

1. Fundamentals

1. Introduction

2. About this Project

3. Applications

4. Job Opportunities

5. Why Python, Keras, and Google Colab

2. Building and Training the Model

1.1 image-enhancement.rar

1. Working directory set up

2. Dataset

3. What is inside Code.ipynb

4. Launch Code

5. Enable the GPU

6. Mount Google Drive in a Google Colab notebook

7. Import various libraries

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

9. Read and preprocess an image

10. Randomly cropping images

11. Loading and preprocessing image data

12. Constructing a TensorFlow dataset pipeline

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

14. Initializes datasets for training and validation

15. Selectively integrate multi-scale features

16. Dynamically learn spatial attention weights

17. Create a channel-wise attention mechanism

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

19. Perform feature extraction

20. Increase the spatial dimensions of the feature maps

21. Multi-scale residual block

22. Recursive residual group

23. Architecture for the Multiple Iterative Residual Network model

24. Custom loss and evaluation metric

25. Compiling

26. Training of the model

27. Saving the trained model

28. Plotting the training and validation loss

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

30. Visualize multiple images

31. Image enhancement using a pre-trained model

32. Visual inspection

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تولید کننده: Udemy