{'fraction_non_alphanumeric': 0.03171641791044776, 'fraction_numerical': 0.016791044776119403, 'mean_word_length': 5.311764705882353, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Colour Image Enhancement by Fuzzy Logic Based on Sigmoid Membership Function

Colour image enhancement plays an important role in image processing, computer vision and pattern recognition. Fuzzy logic techniques one of the methods used for digital image enhancement. In this study, Fuzzy Logic Based-on Sigmoid Membership Function (FLBSMF) was developed to improve lightness and contrast in coloured images. The FLBSMF algorithm was applied to the lightness component by using only the YIQ colour space, and the colour compounds were unchanged. The suggested algorithm was compared with other algorithms, such as fuzzy logic enhancement using membership function modification based on square operation, fuzzy logic based on histogram, histogram equalisation and multiscale retinex with colour restoration by using different criteria. Form the result the FLBSMF succeeded in enhancing colour images and it had good average values for entropy (7.44), mean square error in saturation (2.2E-08) and hue (8.11E-07), nature image quality (2.97) and lightness order error (0.90).