Analysis of Various Image Preprocessing Techniques for Denoising of Flower Images - Patel, Isha; Patel, Sanskruti; Patel, Atul (International Journal of Computer Sciences and Engineering, 2025)
Identification, and classification of flower images is a crucial issue faced by academicians and researchers. The manual process to distinguish different flower images is a complex task and found difficult for novice persons. A process of extraction, analysis, and understanding of useful information from images is accomplished by an automated process using Computer vision. It basically aims to model, replicate and exceed human vision using computer hardware and software. Image processing techniques may help to recognize a flower image for further identification and classification of them in different species. The fundamental step in image processing is image preprocessing that is applied to improve the quality of images and removing the irrelevant noises existed in images. This paper represents a comparative analysis of different image preprocessing techniques implemented on flower images. The performance evaluation of these techniques is based on their potential to remove noise in flower images. For performance evaluation, Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) methods are used.