A Study on Implementation and Performance Analysis of Basic and Advanced Image Processing Techniques Using Python and OpenCV
DOI:
https://doi.org/10.65126/jocosir.v2i2.60Keywords:
Digital Image Processing, Opencv, Grayscale, Morphological Operation, ConvolutionAbstract
Abstract— Digital image processing plays a crucial role in artificial intelligence and computer vision, with widespread applications in healthcare, agriculture, security, industry, and transportation. This research focuses on implementing both basic and advanced image processing methods using Python and the OpenCV library within a desktop application. The main problem addressed is the lack of an integrated, structured approach that bridges basic and advanced techniques, limiting users' comprehensive understanding of image processing workflows. The objective is to design a complete system that allows step-by-step processing, starting from grayscale conversion, binarization, arithmetic and logical operations, to convolution and morphological transformations such as Sobel edge detection and erosion. The proposed application utilizes Tkinter for the user interface, enabling users to upload images, apply various processing techniques, and analyze results interactively. The system also includes histogram visualization and equalization to enhance contrast. Findings show that the implemented methods effectively transform images in accordance with theoretical expectations, such as edge enhancement and shape simplification. The integration of these methods into a single, user-friendly platform supports both educational and applied uses. The contribution of this research lies in its practical demonstration of digital image processing techniques, providing a comprehensive and accessible reference for developers, researchers, and students. Despite its achievements, the system lacks advanced segmentation and real-time capabilities, which are suggested for future development through integration of adaptive methods and machine learning techniques.
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