SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network

interpretable machine learning models have recently received a considerable attention for their capability to provide understandable explanations of predictions evoked from complex systems. in this paper a novel automatic interpretable classification scheme is introduced based on a Fuzzy Cognitive Map (FCM). The proposed approach aims to address the problem of image classification using high-level features, extracted from a Convolutional Neural Network (CNN). The proposed FCM constitutes a fuzzy-graph structure for representing causal reasoning concerning semantic concepts of the real world, as these are depicted within different images. An advantage over current classifiers is that the proposed FCM embeds a mechanism for automatic determination of its structure from the datasets used in each experiment. More importantly, it provides interpretable classification results, whereas it is simple to implement. Experimental results using publicly available datasets show that the proposed approach efficiently extends the application of the traditional FCMs into image classification problem.