Acquisition of High-Quality Image by Using Maximum Correntropy Criterion Kalman Filter

Reconstructing a 3D shape using one or several images is one of the important factors in implementing a digital twin in the field of smart farm. Shape from Focus (SFF) is a passive method that reconstructs a 3D shape using 2D images having different focus levels. When 2D images are acquired at each step along the optical axis, mechanical vibrations occur. SFF techniques are vulnerable to jitter noise that changes the focus values of 2D images. In this manuscript, a new filtering technique that provides high accuracy and low computational cost for 3D shape recovery is proposed. First, jitter noise is modeled as a Lévy distribution. This assumption makes it possible to show the effectiveness of the proposed filtering technique in the presence of non-Gaussian noise. Second, the focus curves are modeled with a Gaussian function to compare the performance of the proposed filtering technique and the existing filtering techniques. Finally, the maximum correntropy criterion Kalman filter is designed and applied to the modeled focus curves. The experimental results demonstrate the effectiveness of proposed method.