Morphological enhancement of microscopy mineral image using opening- and closing-based toggle operator

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

Microscopy mineral image enhancement through center operator construction

Enhancing a microscopy mineral image to produce clear and rich details is important for mineral analysis. To enhance effectively, an algorithm utilizing the constructed center operator is proposed in this paper. First, the center operator is constructed from the opening and closing based toggle operator. Second, the mineral image features are extracted by using the constructed center operator. Third, the multiscale mineral features are extracted through multiscale morphological theory using the multiscale structuring elements. Fourth, the final features for mineral image enhancement are calculated from the extracted multiscale features based on the weight strategy. Finally, the mineral image is effectively enhanced through importing the final features into the original mineral image. Experimental results on various microscopy mineral images verified that the proposed algorithm performed well for enhancement and had competing performance compared with some existing algorithms.