Depth from defocus (DFD) is a technique that restores scene depth based on the amount of defocus blur in the images. DFD usually captures two differently focused images, one near-focused and the other far-focused, and calculates the size of the defocus blur in these images. However, DFD using a regular circular aperture is not sensitive to depth, since the point spread function (PSF) is symmetric and only the radius changeswith the depth. In recent years, the coded aperture technique, which uses a special pattern for the aperture to engineer the PSF, has been used to improve the accuracy of DFD estimation. The technique is often used to restore an all-in-focus image and estimate depth in DFD applications. Use of a coded aperture has a disadvantage in terms of image deblurring, since deblurring requires a higher signal-to-noise ratio (SNR) of the captured images. The aperture attenuates incoming light in controlling the PSF and, as a result, decreases the input image SNR. In this paper, we propose a new computational imaging approach for DFD estimation using focus changes during image integration to engineer the PSF.We capture input imageswith a higher SNR since we can control the PSF with a wide aperture setting unlike with a coded aperture. We confirm the effectiveness of the method through experimental comparisons with conventional DFD and the coded aperture approach.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition