Dazun Zhao

Implementing color transformation across media based on color appearance model by neural networks

Interest in color appearance models (CAM) has been greatly stimulated recently by the need in handling digital images. This article demonstrates that a multi-layers feed-forward artificial neural network with the error back-propagation algorithm was used to approximate color appearance model CIECAM02 with different white points and different media. For the prediction of the forward and inverse model respectively, in order to realize accurate mapping, especially to the inverse model, color spaces conversion between input color space and output color space (that is cylindrical coordinates and rectangular coordinates) was implemented before training the neural networks. Meanwhile we approximated the combination of the forward and inverse CIECAM02 models employing a neural network for different conditions including whites (D65 or D50) and media (booth and CRT) in order to realize the color transformation from one medium to another conveniently. The experimental results indicated that the prediction could satisfy the accuracy requirement. So in practice we can choose these two kinds of different prediction ways to meet our need according to different situations.

Hybrid algorithm in phase diversity wavefront sensing

Phase diversity (PD), proposed by Gonsalves, is a kind of wavefront sensing technique based on measurement of two or more images of object. The optical system involved is relatively simple. It makes use of the methods of optimization and image processing, which can jointly estimate phase aberration as well as object itself simultaneously. The most significant characteristic of this technique is that it works well with extended scenes. Steepest descent method and conjugate gradient method both are preferable algorithms for nonlinear optimization. As a matter of fact, any one of two methods has some limitations. Steepest descent method is a local property and conjugate gradient methods convergence rate is slow. Combining two methods to develop a mixed algorithm, we can avoid entrapping into a local minimum and raise global convergence rate. Simulation results demonstrate that the hybrid algorithm has the features of quick convergence rate, comparatively large convergence range, which make the method of phase diversity remarkably robust and numerically efficient.

Phase retrieval based wavefront sensing experimental implementation and wavefront sensing accuracy calibration

As a wavefront sensing (WFS) tool, Baseline algorithm, which is classified as the iterative-transform algorithm of phase retrieval, estimates the phase distribution at pupil from some known PSFs at defocus planes. By using multiple phase diversities and appropriate phase unwrapping methods, this algorithm can accomplish reliable unique solution and high dynamic phase measurement. In the paper, a Baseline algorithm based wavefront sensing experiment with modification of phase unwrapping has been implemented, and corresponding Graphical User Interfaces (GUI) software has also been given. The adaptability and repeatability of Baseline algorithm have been validated in experiments. Moreover, referring to the ZYGO interferometric results, the WFS accuracy of this algorithm has been exactly calibrated.

Modified Wavefront Sensing and Correction Methodology for Active Segment Mirror of Large Space Telescope

The active segmented primary mirror of space telescope needs realignment and figure correction after deployment. A wavefront sensing and correction methodology, including calculation based correction, is proposed to separate piston error from figure errors at exit-pupil.

Phase retrieval in annulus sector domain by non-iterative methods

Phase retrieval could be achieved by solving the intensity transport equation (ITE) under the paraxial approximation. For the case of uniform illumination, Neumann boundary condition is involved and it makes the solving process more complicated. The primary mirror is usually designed segmented in the telescope with large aperture, and the shape of a segmented piece is often like an annulus sector. Accordingly, It is necessary to analyze the phase retrieval in the annulus sector domain. Two non-iterative methods are considered for recovering the phase. The matrix method is based on the decomposition of the solution into a series of orthogonalized polynomials, while the frequency filtering method depends on the inverse computation process of ITE. By the simulation, it is found that both methods can eliminate the effect of Neumann boundary condition, save a lot of computation time and recover the distorted phase well. The wavefront error (WFE) RMS can be less than 0.05 wavelength, even when some noise is added.

Hybrid phase-diverse phase retrieval algorithm in wavefront sensing for segmented optics

Wavefront sensing of the segmented optics usually includes two steps. The first step is to sense and correct each segments position error (co-phasing error) and figure error (aberration), and the second step is to sense the residual wavefront error over the whole aperture. However, due to the limited correction capability and accuracy of the figure sensing and co-phasing sensing in the first step, the original fine wavefront sensing (WFS) algorithm in the second step, the Modified Baseline algorithm, would be failed once the absolute figure difference at nearby edges of nearby segments exceeds λ/2. Thus a Hybrid Phase-diverse Phase Retrieval (HPDPR) algorithm, which adds the Levenburg-Marquardt (LM) algorithm to eliminate each segments residual position error for decreasing the absolute figure difference, is proposed. Simulations have proven the validity of the HPDPR algorithm, and shown its robustness and high WFS accuracy.

Visual modeling of cathode ray tube displays for finding cross-media corresponding colors based on the natural color system color atlas and neural networks

A visual method for characterizing cathode ray tube (CRT) displays and finding corresponding colors is presented. The method is motivated by the considerable expense of measuring instruments and the consideration of color appearance factors. In the experiment, a set of color chips in the natural color system (NCS) color atlas, an illuminant, and a CRT are used. 487 sample pairs of RGB (CRT) and HVC, which are the attributes of NCS color chips, are obtained by experimentally comparing softcopies and hardcopies in the office environment in the sense of best color matching to nine observers. Both the spectral data of the color chips and the colorimetric values of the CRT samples are measured. In addition, 12 error back-propagation (BP) neural networks are trained to help the realization of the transformation from HVC to RGB for data generalization. The current results show that the displays on the CRT can match the chips in color perception well.