What you will learn:
- Understanding the basic principles of deep learning and its applications in image enhancement.
- Discover practical techniques for preprocessing and increasing the volume of image data using Python libraries.
- Implementation of deep learning models for image enhancement tasks.
- Mastering the use of Cross and TensorFlow frameworks for building and training deep learning models.
- Using Google Colab for integrated development, training and evaluation of deep learning models in a cloud-based environment.
- Learn about advanced concepts such as selective kernel feature fusion, spatial and channel attention mechanisms, and multiscale residual blocks to achieve excellent results in image enhancement.
- Apply learned techniques to real-world scenarios and datasets, reinforcing practical skills through hands-on projects and exercises.
- With the skills and knowledge gained from the course, prepare for high-paying career opportunities in fields such as computer vision, image processing, and machine learning.
At the end of this course, students will have the expertise to perform complex image enhancement tasks using deep learning techniques and tools. With practical experience and theoretical understanding, graduates will be well-positioned for employment opportunities in industries requiring expertise in image processing and deep learning technologies.
Who is this course suitable for?
- People who are interested in learning Python programming for image enhancement and low light photography.
- Students majoring in computer science, data science, or related fields with a focus on image processing and computer vision.
- Professionals looking to improve their skills in image enhancement techniques, especially in low light photography.
- Photography enthusiasts and enthusiasts who are interested in exploring techniques to improve image quality in challenging lighting conditions.
