Light field near eye displays (LFNED) can produce lightweight devices and address the accommodation-convergence conflict. However, low spatial resolution creates a poor immersive experience in LFNED. In addition, although many e-shifting devices have been proposed to enhance resolution in projection system, yet those devices are too bulky to be applied in an LFNED to keep it lightweight. In this paper, a compact e-shifting component is proposed to enhance image resolution in an LFNED by using a birefringent plate and twisted nematic switch cell. The proposed e-shifting device is a flat and thin component with only 2.6 mm of thickness, which could be placed in the gap of an LFNED without increasing the thickness. The results show that the proposed components could be easily integrated in an LFNED with the result of resolution enhancement.

An optimized Light Field (LF) VR display with ultra‐high pixel density LCD (3.1 inch 1411 PPI) is presented. Field of view (FOV) and the experience of immersion are improved by adopting the 3%3% resolution display panel. Functions of vision correction (glasses free), reduced Vergence‐Accommodation‐Conflict (VAC) and enlarged eye box with eye tracking are also realized.

The degenerated performance of extend depth of field (EDoF) in wavefront coding system which using cubic phase mask is simulated. A periodical rotationally symmetric surface error structure is presented and combined with comparison the similarity of point spread function (PSF). The peak to valley (PV) error of the cubic surface is needed smaller than 15% compare with the sag of the cubic surface for low period error existed.

This paper develops a digital decoding design for the imaging system with phase coded lens. The phase coded lens is employed to extend the depth of filed (DoF), and the proposed design is used to restore the special-purpose blur caused by the lens. Since in practice the imaging system inevitably contains manufacturing inaccuracy, it is often difficult to obtain precise point spread function (PSF) for image restoration. To deal with this problem, we develop a flow for designing filters without PSF information. The imaging system first takes a shot of a well-designed test chart to have a blur image of the chart. This blur image is then corrected by using the perspective transformation. We use both of the image of the test chart and the corrected blur image to calculate a minimum mean square error (MMSE) filter, so that the blur image processed by the filter can be very alike to the test chart image. The filter is applied to other images captured by the imaging system in order to verify its effectiveness in reducing the blur and for showing the capability of extending the DoF of the integrated system.Keywords: phase coding, extended depth of field, computational imaging, digital decoding, image restoration, minimum mean square error filter

Computational imaging has been using for depth of field extension, distance estimation and depth map for stereo imaging and displaying with great successfully, which are realized by using special designed imaging lens and optimized image post-processing algorithm. Several special coding structures have been presented, like cubic, generalized cubic, logarithmic, exponential, polynomial, spherical and others. And different image post-processing algorithms like Wiener filter, SVD method, wavelet transform, minimum mean square error method and others are applied to achieve jointly-optimization. Although most of studies have shown excellent invariant of optical transfer function for imaging lens, but such invariance will be unsatisfied when manufacturing errors are considered. In this paper, we present a method to consider behavior of tolerance in computational imaging system from pure optical to optical -digital, which means lens and image post-processing are both included. An axial irradiance equalization phase coded imaging system is illustrated for tolerance sensitivity by using similarity of point spread function (PSF), Strehl ratio (SR), and root mean square error (RMSE) of restored images. Finally, we compare differences between presented method and Zemax.