Three-level image demonstration with optimized multi-feature fuzzy clustering and EPAPCNN system

The multiresolution-based fusion techniques can be developed with innovative image preprocessing and fusion techniques. In this proposed work, the three-level decomposition in image preprocessing and optimized multi-feature fuzzy clustering (OMFC) for extracting features, fusing features and reconstructing image are done. Each source images are decomposed into equivalent smooth, structure and boundary levels with the help of spatial domain. Next, the large and small frequency bands are merged by applying enhanced parametric adaptive pulse coupled neural network technique for preserving significant information and data extraction. At last, reconstructing target image from the fused image by applying inverse OMFC is performed, and the validations are done in various categories. Demonstration of the proposed technique is given, and the results shows its performance better than the other existing techniques.

Automated summary

The innovative image preprocessing and fusion techniques are developed based on multiresolution-based fusion techniques. The proposed work involves three-level decomposition in image preprocessing and optimized multi-feature fuzzy clustering (OMFC) for feature extraction, fusion, and image reconstruction. The source images are decomposed into smooth, structure, and boundary levels using spatial domain. The large and small frequency bands are merged using enhanced parametric adaptive pulse coupled neural network technique for preserving significant information and data extraction. Reconstruction of the target image is performed using inverse OMFC, and validations are done in various categories. The demonstration of the proposed technique shows better performance compared to existing techniques.