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DESIGN AND IMPLEMENTATION OF A PICTURE ENHANCER AND COLORIZATION USING MACHINE LEARNING
- 1. DESIGN AND IMPLEMENTATION OF A PICTURE ENHANCER AND COLORIZATION USING MACHINE LEARNING BY OGUNGBEMI DEBORAH OMOBOLANLE MATRIC NO: 21CS1060 DEPARTMENT OF COMPUTER SCIENCE FACULTY OF NATURAL SCIENCE PRINCE ABUBAKAR AUDU UNIVERSITY, ANYIGBA, KOGI STATE SUPERVISOR: MR AKONMODE RICHARD
- 2. Introduction In the digital imaging era, there is a growing demand for tools capable of transforming monochromatic or grayscale images into vibrant, realistic coloured versions. This project seeks to address this need by designing and implementing a picture enhancer and colourization system using advanced machine learning techniques, specifically leveraging OpenCV, a prominent library for image processing tasks.
- 3. Statement Of Problems Grayscale images lack the visual appeal and realism present in their colored counterparts. Existing colorization methods often produce unnatural or unrealistic results. Limited user-friendly tools are available for enhancing and colorizing images without extensive technical expertise.
- 4. AIM The primary aim of this project is to design and implement a machine learning-based picture enhancer and colorization system
- 5. OBJECTIVES To integrate the detail enhancement and colorization modules into a unified picture enhancer. To design and implement a deep learning model for enhancing image details. To develop a colorization model using machine learning algorithms.
- 6. SIGNIFICANCE OF THE STUDY The research on designing and implementing a picture enhancer and colorization system using machine learning techniques holds significant importance across various domains. By leveraging deep learning algorithms, this technology aims to enhance and colorize images automatically with unprecedented accuracy and realism, revolutionizing fields like computer vision, photography, film production, digital art, and cultural heritage preservation. It enables new creative possibilities for content creators, provides visually striking product images for e-commerce and advertising, and offers opportunities for preserving historical artifacts.
- 7. SCOPE OF THE STUDY This study will focus on the enhancement and colorization of static images. The developed system will support various image formats and resolutions. The project will not address real-time video processing or 3D image enhancement.
- 8. Literature review S/N Author(s) Year Title Knowledge Gap Contribution to Knowledge 1 Lehtinen et al. 2018 Noise2Noise Traditional denoising methods may struggle with complex noise distributions. Proposed a GAN-based method for image denoising that can handle complex noise distributions without requiring clean target data. 2 Mukherjee et al. 2019 EGAN Traditional denoising methods may struggle with complex noise distributions. Proposed an Encoder-Decoder GAN (EGAN) for image denoising that can handle complex noise distributions. 3 Zhang et al. 2017 DnCNN Traditional denoising methods may not effectively capture noise patterns and representations. Introduced a Convolutional Neural Network (CNN) architecture, DnCNN, for effective image denoising by learning noise representations and patterns. 4 Tai et al. 2017 MemNet Traditional denoising methods may not effectively capture noise patterns and representations. Proposed MemNet, a CNN-based architecture with memory blocks for image denoising, demonstrating state-of-the-art performance. 5 Dong et al. 2016 SRCNN Traditional image super-resolution methods may produce blurry or artifacts in upscaled images. Introduced SRCNN, one of the first deep learning- based super-resolution models using Convolutional Neural Networks, paving the way for improved image upscaling.
- 9. REASERCH METHODOLOGY The approach employed in this project follows the SSADM (Structured Analysis and Design Methodology). SSADM is a methodology focused on the design of information systems, originally developed in the early 1980s in the UK by the Central Computer and Telecommunications Agency (CCT). It stands as the standard method endorsed by the UK government for conducting system analysis and design in information technology projects. Historically, SSADM has been applied primarily to the development of medium or large- scale systems. Notably, there is a variant called "Micro SSADM" tailored for smaller systems. The SSADM process commences with defining the information system strategy and progresses through stages such as a feasibility study module, requirements analysis, requirements specification, logical system specification, and ultimately concludes with a final physical system design.
- 11. ANALYSIS OF THE EXISTING SYSTEM Whilst Adobe Photoshop offers tools for enhancing and colourising greyscale images, the process often involves laborious manual intervention and can be inefficient, particularly for large-scale projects. Techniques like the "Colorize" adjustment layer provide basic colourisation but lack natural colour reproduction, while selective colourisation using selection tools and layer masks requires significant skill and expertise, especially for intricate details.
- 12. DISADVANTAGES OF THE EXISTING SYSTEM Extensive Manual Effort: The process of enhancing and colorising images in Photoshop frequently necessitates substantial manual intervention, rendering it time-consuming and labour-intensive, particularly for large-scale projects or intricate scenes. Lack of Automation: Photoshop's tools do not offer a fully automated solution for colorising greyscale images, requiring manual adjustments for each individual image. Limited Natural Colour Reproduction: The "Colorize" adjustment layer in Photoshop allows for basic tinting of greyscale images but lacks the capability to accurately reproduce natural colours and details.
- 13. Analysis of the Proposed System The proposed system utilises advanced machine learning algorithms, specifically deep learning and computer vision libraries like OpenCV, to address the limitations of traditional image editing tools. By training neural networks on extensive datasets of colour and greyscale image pairs, the system can automate the colourisation and enhancement process, rapidly mapping greyscale inputs to their colourised counterparts while preserving intricate details and reproducing natural colours consistently.
- 14. Advantage of the Proposed System Automate the colourisation and enhancement process. Rapidly map greyscale inputs to their colourised counterparts while preserving intricate details and reproducing natural colours consistently. Offer adaptability to diverse image types, styles, and domains.
- 15. USE CASE OF THE PROPOSED SYSTEM
- 16. FLOW CHART OF THE PROPOSED SYSTEM
- 17. DATA FLOW DIAGRAM OF THE PROPOSED SYSTEM