Improving Depth Computation From Robust Focus Approximation

Shape-from-focus (SFF) technique is to recover three-dimensional shape of an object from a sequence of two-dimensional images of the same object taken by gradually changing the focus settings of the imaging device. In SFF, for each pixel location (i, j), a focus measure operator computes sharpness (focus) value on a small neighborhood of the pixel (i, j) at each image along the z-axis (optical axis). The image sensor position z that has the maximum focus value and camera parameter information provide the distance information between the lens and the object point corresponding to the pixel (i, j). In traditional SFF methods, the final focus value of each pixel is determined by an average value of the initial focus values at the neighborhood of the pixel to remove the noise effects. However, it only reduces the noise effects instead of completely removing it. In this paper, we proposed a noise filtering technique that tries to remove noises while computing the focus values. First, an initial focus measure volume is computed by applying one of the focus measures on each pixel in the image sequence. Then, the focus value at each pixel is examined whether it is corrupted by noise or not by analyzing the neighboring focus values. The noisy focus value of the pixel is re-computed from noise-free focus values of neighboring pixels. The experimental results conducted on both the synthetic and real-world objects show the proposed method produces the better three dimensional shape in comparison to the existing methods.