A comprehensive approach to image cartoonization using edge detection and morphological operations
Manish Nandy, Kapesh Subhash Raghatate · 2025
Image cartoonization converts standard photographs into stylized depictions similar to hand-drawn or animated cartoons. The proposed work introduces a method using OpenCV to achieve effective image cartoonization. Initially, the image is loaded and converted to grayscale, which simplifies subsequent processes and reduces complexity for efficient edge detection and noise reduction using techniques such as Gaussian blur. Canny edge detection is then applied to identify essential features and outlines. Morphological operations like dilation and erosion follow to refine and clarify the edges. These refined edges are combined with the smoothed grayscale image using bitwise operations, which enhances the cartoon effect by creating bold outlines. Furthermore, the proposal examines techniques to improve colors and textures, resulting in a vivid cartoon effect through edge masking and advanced image processing methods. This approach integrates edge detection and color enhancement to strike a balance between maintaining important details and introducing stylized cartoon elements. Experimental results confirm the effectiveness of this method, showing its capability to produce high-quality cartoon-like images. The proposed methodology provides a structured framework for image cartoonization, applicable in enhancing stylization techniques in digital art, entertainment, and visual communication. By utilizing the versatility and robustness of OpenCV, this approach offers a practical solution for artists and developers aiming to automate and refine the cartoonization process, enabling innovative applications across various creative and technical fields. The successful implementation of this method highlights its practicality and adaptability, making it a valuable tool for both professionals and amateurs in transforming photographs into visually engaging cartoon-style images.