A feature analysis approach to estimate 3D Shape from Image Focus

This paper introduces a new robust algorithm for shape from focus (SFF). Principal component analysis (PCA) is applied to transform the data into eigenspace and the first feature is employed to calculate the depth value. Contrary to computing the focus value locally by a focus measure in first step and then, in second step, approximating the depth map, the proposed method finds the location of the best focused value over a sequence of pixels. The proposed method is experimented using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Two other global statistical metrics root mean square error (RMSE) and correlation have also been applied for synthetic image sequence. Experimental results have demonstrated the effectiveness and the robustness of the new method.