Low-Light Image Enhancement and Deep Learning with Python

Low-Light Image Enhancement and Deep Learning with Python, available at $54.99, has an average rating of 5, with 37 lectures, based on 1 reviews, and has 103 subscribers.

You will learn about 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. This course is ideal for individuals who are Individuals interested in learning Python programming for image enhancement and low-light photography. or Students pursuing studies in computer science, data science, or related fields with a focus on image processing and computer vision. or Professionals seeking to enhance their skills in image enhancement techniques, particularly in the context of low-light photography. or Hobbyists and enthusiasts passionate about photography and interested in exploring techniques to improve image quality in challenging lighting conditions. It is particularly useful for Individuals interested in learning Python programming for image enhancement and low-light photography. or Students pursuing studies in computer science, data science, or related fields with a focus on image processing and computer vision. or Professionals seeking to enhance their skills in image enhancement techniques, particularly in the context of low-light photography. or Hobbyists and enthusiasts passionate about photography and interested in exploring techniques to improve image quality in challenging lighting conditions.

Enroll now: Low-Light Image Enhancement and Deep Learning with Python

Summary

Title: Low-Light Image Enhancement and Deep Learning with Python

Price: $54.99

Average Rating: 5

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: ₹799

Quality Status: approved

Status: Live

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.

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Fundamentals

Lecture 1: Introduction

Lecture 2: About this Project

Lecture 3: Applications

Lecture 4: Job Opportunities

Lecture 5: Why Python, Keras, and Google Colab?

Chapter 2: Building and Training the Model

Lecture 1: Working directory set up

Lecture 2: Dataset

Lecture 3: What is inside Code.ipynb?

Lecture 4: Launch Code

Lecture 5: Enable the GPU

Lecture 6: Mount Google Drive in a Google Colab notebook

Lecture 7: Import various libraries

Lecture 8: Sets random seed and defines image size and batch size

Lecture 9: Read and preprocess an image

Lecture 10: Randomly cropping images

Lecture 11: Loading and preprocessing image data

Lecture 12: Constructing a TensorFlow dataset pipeline

Lecture 13: Defining file paths for training, validation, and test datasets

Lecture 14: Initializes datasets for training and validation

Lecture 15: Selectively integrate multi-scale features

Lecture 16: Dynamically learn spatial attention weights

Lecture 17: Create a channel-wise attention mechanism

Lecture 18: Combines both channel-wise and spatial-wise attention mechanisms

Lecture 19: Perform feature extraction

Lecture 20: Increase the spatial dimensions of the feature maps

Lecture 21: Multi-scale residual block

Lecture 22: Recursive residual group

Lecture 23: Architecture for the Multiple Iterative Residual Network model

Lecture 24: Custom loss and evaluation metric

Lecture 25: Compiling

Lecture 26: Training of the model

Lecture 27: Saving the trained model

Lecture 28: Plotting the training and validation loss

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

Lecture 30: Visualize multiple images

Lecture 31: Image enhancement using a pre-trained model

Lecture 32: Visual inspection

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

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