EXPLORING AI-BASED TECHNIQUES FOR IMAGE PROCESSING USING STREAMLIT APPLICATION

Abstract

Abstract: In this paper, we delve into the realm of image processing using AI-based techniques, with a focus on leveraging the Streamlit application for development and deployment. Our exploration encompasses a wide array of methodologies, ranging from traditional image enhancement algorithms to cutting-edge deep learning architectures. Through a series of experiments, we demonstrate the efficacy of AI-based approaches in addressing various challenges in image processing, including denoising, super- resolution, segmentation, and classification. The scope of our experiments extends to diverse domains, including medical imaging, remote sensing, and digital photography. Significant data obtained from benchmark datasets and real-world scenarios serve as the foundation for our analyses. Our findings underscore the transformative potential of AI-driven image processing techniques, showcasing their ability to enhance image quality, extract meaningful information, and facilitate decision-making processes. Furthermore, we present insights into the practical implications of deploying Streamlit applications for rapid prototyping and deployment of image processing solutions. Through comprehensive evaluations and comparisons with existing methodologies, we elucidate the strengths and limitations of AI-based techniques, paving the way for future advancements in this burgeoning field. IndexTerms – Python, Machine Learning, Deep Learning, Open CV, Matplotlib.