3D shape recovery from image defocus using wavelet analysis
We propose a new method for depth from defocus (DFD) using wavelet transform. Most of the existing DFD methods use inverse filtering to determine the measure of defocus. These methods suffer from inaccuracies in finding the frequency domain representation due to windowing and border effects. The proposed method uses wavelets that allow performing both the local analysis and windowing with variable-sized regions for images with varying textural properties. We show that normalized image ratio of wavelet power by Parseval's theorem is closely related to blur parameter and depth. Experimental results have been presented demonstrating that the proposed DFD method is faster and generates more accurate depth maps than the previous methods.