Low-Light Image Enhancement and Deep Learning with Python
Elevating Low-Light Photography with Python, Keras, Tensorflow, and Google Colab: A Deep Learning Hands-on Approach
4.75 (4 reviews)
117
students
1.5 hours
content
Jun 2024
last update
$13.99
regular price
What you will 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.
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5867038
udemy ID
11/03/2024
course created date
22/06/2024
course indexed date
Angelcrc Seven
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