Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan.
Department of Computer Science, Abbottabad University of Science and Technology, Abbottabad 22010, Pakistan.
Sensors (Basel). 2022 Apr 1;22(7):2724. doi: 10.3390/s22072724.
The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme's effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.
图像的散焦或运动效果是数字图像模糊区域的主要原因之一。它会在一定程度上影响图像伪影。然而，需要进行自动散焦分割，以便将模糊区域和清晰区域分开，从而提取特定区域中有关散焦模糊对象的信息，例如场景增强和在散焦模糊图像中的对象检测或识别。在噪声存在的情况下，散焦模糊分割算法的存在不太明显，而且设计局部清晰度度量图的成本也很高。在这项研究中，作者提出了一种新颖而强大的散焦模糊分割方案，该方案由局部三元模式（LTP）和脉冲耦合神经网络（PCNN）技术组成。所提出的方案将模糊区域从图像场景中的模糊碎片中分割出来，以解决现有散焦分割方法存在的上述局限性。结果表明，与参考算法相比，所提出的清晰度测量的上下模式融合在区域和边缘方面可以产生更明显的结果。此外，所提出的描述符中的建议参数可以灵活修改，以进行多种设置。为了测试所提出方案的有效性，将其与八个参考技术以及由多个类别的 1000 张半模糊图像组成的散焦模糊数据集进行了实验比较。该模型采用了包括精度、召回率和 F1 得分在内的各种评估指标，提高了所提出方案的效率和准确性。此外，所提出的方案还使用了其他一些评估参数，例如准确率、马修斯相关系数（MCC）、骰子相似系数（DSC）和特异性，以确保可证明的评估结果。此外，还观察到基于距离平均解的评价方法（EDAS）模块的模糊逻辑排名方法在散焦模糊分割的有前途的完整性分析中，以及在最小化时间复杂度方面也具有优势。
