Multi-Aperture Depth Map Using Blur Kernels and Down-Sampling

Abstract

Embodiments relate to different methods for reducing computations used to estimate depth information. One aspect relates to using down-sampled blur kernels. Another aspect relates to processing of edges in the images. Yet another aspect relates to using partial blur kernels, such as single-sided blur kernels. Yet another aspect relates to frequency filtering to reduce energy and noise at frequencies that do not distinguish between different blur kernels.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S)[0001]This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62 / 121,203, “Dual-Aperture Depth Map Using Adaptive PSF Sizing,” filed Feb. 26, 2015. The subject matter of all of the foregoing is incorporated herein by reference in its entirety.BACKGROUND[0002]1. Field of the Invention[0003]This invention relates to a multi-aperture imaging system that uses multiple apertures of different f-numbers to estimate depth of an object.[0004]2. Description of Related Art[0005]A dual-aperture camera has two apertures. A narrow aperture, typically at one spectral range such as infrared (IR), produces relatively sharp images over a long depth of focus. A wider aperture, typically at another spectral range such as RGB, produces sometimes blurred images for out of focus objects. The pairs of images captured using the two different apertures can be processed to generate distance information of an object, for ...

AI Technical Summary

Benefits of technology

Embodiments of the patent text describe methods for reducing computational requirements for estimating depth information. One method is to use smaller blur kernels for depth processing by dividing the distance range into sub-ranges and using a bank of blur kernels for each sub-range. The captured images are then down-sampled by different factors to further reduce computation. Another method involves processing image edges by normalizing them to match phases and energies and then binarizing them to simplify depth estimation. Additionally, frequency filtering is used to reduce energy and noise at frequencies that are not useful for distinguishing between different blur kernels. These methods provide efficient and accurate depth estimation, particularly for large images.

Problems solved by technology

However, conventional processing methods can be computationally expensive.

In this way, although the original blur kernels may span a large range of sizes, the down-sampled blur kernels will be more limited in size which reduces computation.

In yet another aspect, frequency filtering is used to reduce energy and noise at frequencies that are not useful to distinguish between different blur kernels.

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