An Exhaustive Review of Neutrosophic Logic in Addressing Image Processing Issues




Neutrosophic Logic, Image Processing, Image Segmentation, Noise Reduction, Image Enhancement


Since the importance of images in our lives and the advancements in computer data gathering methods, anyone can collect a large number of images, but most of them cannot be processed manually. Image processing therefore becomes appealing since various types of data may be represented and processed digitally. Image processing has become the most popular processing method, employed in security camera films, healthcare images, images from remote sensors, and naturalistic image/videos because of fast computers and processors. In order to raise cognitive function and speed up decision-making, image processing is crucial to many information access systems. Since ambiguity now permeates every part of the world, including images, discussing the neutrosophic logic forms the central idea of this discussion, as it is able to handle this ambiguity. To apply the neutrosophic logic, this requires converting the image into neutrosophic reasoning. When using neutrosophic reasoning for image retrieval, average recall and precision measures improve over other approaches. As the image processing field covers several tracks such as image segmentation, noise reduction, image classification, and others. Because there are so many research articles published in this field every year, we thought it would be appropriate to introduce a survey study on this subject. As a result, this study offers a comprehensive assessment of the literature on applying neutrosophic logic to image processing problems that have surfaced during the previous five years (2019–2023).


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How to Cite

Mandour , S. (2023). An Exhaustive Review of Neutrosophic Logic in Addressing Image Processing Issues. Neutrosophic Systems With Applications, 12, 36–55.