Overview / Usage

This project aims to optimize image filtering algorithms using Intel's oneAPI toolkit and deploy them on a cloud-based server. The project leverages parallel processing with oneAPI's Data Parallel C (DPC) to achieve faster and more efficient image filtering. Additionally, it includes a user-friendly web interface to allow users to apply the accelerated image filters on their own images.

Problems Being Solved:

• Performance Bottlenecks: Traditional image filtering algorithms can be computationally intensive, leading to slow processing times for high-resolution images. This project addresses the performance bottlenecks by parallelizing the algorithms with oneAPI, allowing for faster execution on multiple CPU cores.
• Cloud-Based Accessibility: By deploying the project on a cloud server, it eliminates the need for specialized hardware and makes the image filtering acceleration accessible to users without powerful hardware resources.
• Real-Time Image Processing: The project enables real-time image processing, making it useful for applications that require quick image filtering, such as video processing, image editing, and real-time computer vision tasks.
• Scalability: By benchmarking the project's performance with various image sizes and parallelism levels, it demonstrates scalability, ensuring efficient processing regardless of the input image's dimensions.

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How This Work/Research is Experienced or Used in Production:

• Image Editing and Enhancements: Image filtering is a fundamental operation in image editing software. The accelerated image filters can be integrated into image editing tools, allowing users to apply real-time enhancements and effects to their images.
• Real-Time Computer Vision: In computer vision applications, fast image filtering is essential for object detection, edge detection, and feature extraction. The accelerated image filters can be used in real-time computer vision systems for faster analysis.
• Video Processing: Video streams consist of a series of images that require real-time processing. The accelerated image filtering techniques are valuable for video processing applications, enabling quick frame-by-frame analysis.

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Methodology / Approach

1. Project Planning: Defining the project scope, objectives, and deliverables. Seting up a timeline and allocating tasks
2. Image Filtering Algorithm Implementation: Developing image filtering algorithms, such as blur, edge detection, and sharpening, using DPC++ programming languages. Ensuring the algorithms are functional and produce accurate results.
3. Cloud Environment Setup: Choosing a cloud service provider (e.g., AWS, Azure, or Google Cloud) and set up a cloud-based server. Install the necessary development environment, including oneAPI toolkit, libraries, and compilers.
4. Parallelization with oneAPI: Parallelizing the image filtering algorithms using oneAPI's Data Parallel C++. Divide the image processing tasks among multiple CPU cores for faster execution.
5. Performance Benchmarking: Implement a sequential CPU-based version of the image filtering algorithms. Benchmark the performance of the parallelized algorithms against the sequential implementation using a variety of image sizes.
6. Web Interface Development: Creating a web-based interface for users to upload images and select the desired image filtering algorithm. Utilize web technologies like HTML, CSS, and JavaScript for the front-end development.
7. Cloud Integration: Integrating the parallelized image filtering algorithms with the cloud server. Ensuring the algorithms can be executed on the cloud resources efficiently.
8. Visualization: Implementing a visual display of the original and filtered images on the web interface. Allow users to interact with the interface to observe the results and compare the performance.
9. Scalability Testing: Testing the project's scalability by evaluating its performance with various image sizes and different levels of parallelism.

Technologies Used

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1. Intel oneAPI Toolkit: The primary technology for this project, oneAPI provides the necessary libraries, tools, and compilers for parallel programming and optimization.
2. C and DPC: Used for implementing image filtering algorithms and parallelizing the code using oneAPI's Data Parallel C++.
3. Cloud Service Provider: AWS, Azure, or Google Cloud can be utilized to set up the cloud-based server.
4. Web Development: HTML, CSS, and JavaScript for creating the web-based user interface.
5. Benchmarking Tools: Use appropriate tools to measure the performance of the algorithms and compare the results.
6. Visualization Libraries: Libraries like D3.js or Chart.js can be used to visualize the images and their filtered counterparts on the web interface.