IMAGE PROCESSING USING FUZZY LOGIC AND ARTIFICIAL NEURAL NETWORK METHODS

Abstract

This study explores methods for combining fuzzy logic and convolutional neural networks (CNN) to improve image analysis when images are blurry, noisy, or have low contrast. Fuzzy logic is used to represent areas of imprecision in an image by assigning each pixel a degree of membership to different sets, while convolutional neural networks automatically detect important features and identify objects present. Thus, the paper proposes a hybrid Fuzzy-CNN method: pixels or visual features are first «fuzzified» in a logical sense to account for uncertainties, and then fed into the neural network. A defuzzification stage is then applied to the output to obtain a clear and interpretable final result. This approach enhances the reliability of tasks such as segmentation, classification, or image enhancement, particularly in medical or industrial contexts where image quality may be degraded. Tests conducted demonstrate that this hybrid strategy outperforms classical approaches based solely on fuzzy logic or CNN alone, providing higher accuracy and robustness to visual variations and uncertainties. Thus, it represents a solid foundation for more intelligent and adaptive image processing in challenging conditions.