Learned phase coded aperture for the benefit of depth of field extension

Abstract

Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-offs between various system parameters. In such trade-offs, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a different defocus blur for each color channel of the image. The phase-mask is designed such that the CNN model is able to restore from the coded image an all-in-focus image easily. This is achieved by using a joint end-to-end training of both the phase element and the CNN parameters using backpropagation. The proposed approach provides superior performance to other methods in simulations as well as in real-world scenes.

Improving optical properties of remote phosphor LED using green Y2O3:Ho3+ and red Mg4(F)(Ge, Sn)O6:Mn2+ layers

“…The following section is the calculating formula for the unequal SPD of single color LED with Gaussian function [24,25]: (1)…”

Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy

“…Alternatively, it could be used to design optical components and imaging tactics with an optical front end and an algorithmic back end [141]. This could be used to resolve the optimally encoded [142]- [145] patterns for image reconstruction or the extraction of a meaningful metric [141], [146]. This kind of holistic design is considered as the holy grail of computational imaging.…”

What is the depth of field reachable in practice with generic binary phase masks and digital deconvolution?

“…It is well known that this DoF can be extended by adding a co-designed phase mask in the pupil of the lens [1] [2] [3]. Since adding a phase mask tends to decrease the raw image quality, it is possible to perform digital image processing after acquisition to regain optimal quality [4] [5] [6]. In this paper, we study the DoF reachable in practice by generic binary phase masks associated with digital deconvolution based on the average Wiener filter [7].…”