A Novel Defocused Image Segmentation Method Based on PCNN and LBP

The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and non-blurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, to assess better the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.