Authors: Saurabh Sunil Gaikwad, Sanket Nagesh Sonawane, Harshad Abhiman Bansode, Dinesh Datta Jadhav, Suraj Balu Karande, Dr. S. P. Pawar
DOI Link: https://doi.org/10.22214/ijraset.2024.61798
Certificate: View Certificate
Image colorization is a complex and challenging task in computer vision, with numerous applications in art, entertainment, restoration, and more. This project aims to develop an automated image colorization system leveraging the power of deep learning techniques. The primary objective is to train a deep neural network model capable of accurately and semantically colorizing grayscale images, reproducing natural and visually appealing colour distributions. Our approach utilizes Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to learn the intricate relationships between grayscale images and their corresponding colour versions. The project involves the following key steps: Data Collection and Preprocessing, Model architecture, Training, Evaluation, Application. The project\'s outcome is expected to provide a powerful and versatile tool for automating image colorization tasks, offering high-quality results while preserving the artistic intent of the original images. The fusion of deep learning and computer vision techniques in this project exemplifies the potential for artificial intelligence to revolutionize image processing and creative industries.
I. INTRODUCTION
The world is not black and white, yet many historical photographs, classic films, and digital images exist solely in grayscale. The absence of color often limits our ability to fully appreciate and relate to these visual representations. Image colorization, the process of adding lifelike colors to grayscale images, addresses this issue by bringing a new dimension of realism and emotional resonance to visual content.
Historically, manual colorized was the normal, a labor-intensive and time-consuming processes that require skilled artists to painstakingly apply colors to each pixel. However, recent advancements in artificial intelligence and deep learning has ushered in a new era of automation image colorization, making it faster, more accessible, and remarkably accurate?
Colorizeding images is a fascinating field that intersect art and technology, aiming to breath life into historical or black-and-white images by adding color. Through the usage of computer science techniques, particularly deep learning, researchers and enthusiasts has develop algorithm capable of automatically adding color to grayscale images with remarkable accuracy. We delve into the realm of image colorizeding, exploring various methodologies and algorithms used to automate this process. By understanding the underlying principles and implementing these algorithms, we aim to not only gain insights into the intricacies of image processing but also to contribute to the advancement of computer vision techniques.
A. Objective
II. LITERATURE SURVEY
III. METHODOLOGY
A. Data Collection and Preparation
B. Data Preprocessing
C. Model Architecture
D. Loss Functions
E. Training
F. Hyperparameter Tuning
Fine-tune hyperparameters, such as learning rate, batch size, and architectural choices, using the validation dataset to optimize model performance.
G. Evaluation
H. User Interface (Optional)
I. Deployment and Application
J. Documentation and Reporting
A. Conclusion In conclusion, the Image Colorization Project represents a significant step forward in the realm of computer vision and artificial intelligence. This project set out to automate the process of adding lifelike colors to grayscale images, and through the application of deep learning techniques, it has achieved remarkable results. The versatility of this project is evident in its potential applications, ranging from restoring historical photographs to enhancing black-and-white movies, assisting artists in their creative endeavours, and even enabling individuals to effortlessly bring their personal photos to life with vibrant colors. The user-friendly interface developed as part of this project further democratizes image colorization, making it accessible to a wide audience. B. Future Scope 1) Deep Learning Techniques: The core methodology will rely on deep learning techniques, potentially incorporating Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to model the relationship between grayscale and color images. 2) Data Collection: The project will involve the collection and preprocessing of a diverse dataset of grayscale images paired with color references. Data augmentation techniques may be applied to increase dataset diversity. 3) Model Development: The design and development of an effective neural network architecture for colorization, including the choice of loss functions and optimization algorithms, will be a central component of the project.
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Copyright © 2024 Saurabh Sunil Gaikwad, Sanket Nagesh Sonawane, Harshad Abhiman Bansode, Dinesh Datta Jadhav, Suraj Balu Karande, Dr. S. P. Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET61798
Publish Date : 2024-05-08
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here