3D Shape Recovery by Aggregating 3D Wavelet Transform-Based Image Focus Volumes Through 3D Weighted Least Squares

This paper proposes a shape from focus method based on high-frequency components from 3D discrete wavelet transform. First, an input image sequence is decomposed into approximation and detail components up to certain levels. Then, for each level, an image focus volume is obtained from the energy of detail components. These image focus volumes contain varying sized structural information about the shape of the object. From this set of image focus volumes, a single image focus volume is obtained through cross-scale aggregation of image focus volumes by applying 3D weighted least squares. The weights for the smoothness term for each pixel have been computed from the cross-scale prior. Incorporating this cross-scale prior enables the multi-scale interaction for image focus volume aggregation. Finally, the depth map is recovered from the resultant image focus volume using the best focused pixels along the optical axis. The proposed method is evaluated using image sequences of synthetic and real objects. The experimental results demonstrate the effectiveness of the proposed method.