Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Abstract

Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity.