Three-dimensional (3D) cameras are expensive because they employ additional charged coupled device sensors and optical elements, e.g., lasers or complicated scanning mirror systems. One passive optical method, shape from focus (SFF), provides an efficient low cost solution for 3D cameras. However, mechanical vibration of the SFF imaging system causes jitter noise along the optical axis, which makes it difficult to obtain accurate shape information of objects. In traditional methods, this error cannot be removed and increases as the estimation of the shape recovery progresses. Therefore, the final 3D shape may be inaccurate. We introduce an accurate depth estimation method using an adaptive neural network (ANN) filter to remove the jitter noise effects. Jitter noise is modeled by both Gaussian distribution and non-Gaussian distribution. Then, focus curves are modeled by quadratic functions. The ANN filter is designed as an optimal estimator restoring the original position of each frame of the input image sequence in the modeled jitter noise, as a pre-processing step before the initial depth map is obtained. The proposed method was evaluated using image sequences of both synthetic and real objects. Experimental results demonstrate that it is reasonably efficient and that its accuracy is comparable with that of existing systems.

Three-dimensional (3D) shape reconstruction from one or multiple observations is a primary problem of computer vision. Shape from Focus (SFF) is a passive optical method that uses multiple two-dimensional (2D) images with different focus levels. When obtaining 2D images in each step along the optical axis, mechanical vibrations, referred as jitter noise, occur. SFF techniques are vulnerable to jitter noise that can vary focus values in 2D images. In this paper, new filtering method, which provides high accuracy of 3D shape reconstruction and low computational cost, is proposed. First, jitter noise is modeled as Lévy distribution. This assumption makes it possible to show the influence of proposed filtering method in real environment with non-Gaussian jitter noise. Second, focus curves are modeled as Gaussian function to compare the performance of proposed filtering method with those of the conventional filtering methods. Finally, improved maximum correntropy criterion Kalman filter (IMCC-KF) is designed as a post-processing step, and is applied to the modeled focus curves. The experiments are performed on real and synthetic objects and the results demonstrate the effectiveness of proposed method.

Shape from focus is a passive optical technique that uses multiple images with different focus levels. To compute the sharpness of each pixel in the image sequence, obtained by translating the object plane at a constant step size along the optical axis, focus measure operators are used. However, the focus measure operators do not provide the accurate focus value of the center pixel due to information loss between consecutive frames. In this manuscript, Local Regression is proposed as a new fitting method for the focus curves. This method fits the focus curves, obtained by using one of the focus measure operators, by utilizing the weighted least squares regression as non-parametric regression. Experiments are performed on synthetic and real objects to demonstrate the effectiveness of the proposed method.