Abstract
Granular information processing is one of the human-inspired problem-solving aspects of natural computing, as information abstraction is inherent in human thinking and reasoning processes, and plays an essential role in human cognition. Among the different facets of natural computing fuzzy sets, rough sets and their hybridization are well accepted paradigms that are based on the construction, representation and interpretation of granules, as well as the utilization of granules for problem solving. These tools are also known as primary constituents of soft computing whose objective is to provide flexible information processing capability for handling real-life ambiguous situations. They have been successfully employed in various image processing tasks, including image segmentation, enhancement and classification, both individually or in combination with other computing techniques. The reason of such success is rooted to the fact that they provide powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks.
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