Po-Chang Chen

Digital Decoding Design for Phase Coded Imaging System

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.

Mobile phone imaging module with extended depth of focus based on axial irradiance equalization phase coding

This paper presents a mobile phone imaging module with extended depth of focus (EDoF) by using axial irradiance equalization (AIE) phase coding. From radiation energy transfer along optical axis with constant irradiance, the focal depth enhancement solution is acquired. We introduce the axial irradiance equalization phase coding to design a two-element 2-megapixel mobile phone lens for trade off focus-like aberrations such as field curvature, astigmatism and longitudinal chromatic defocus. The design results produce modulation transfer functions (MTF) and phase transfer functions (PTF) with substantially similar characteristics at different field and defocus positions within Nyquist pass band. Besides, the measurement results are shown. Simultaneously, the design results and measurement results are compared. Next, for the EDoF mobile phone camera imaging system, we present a digital decoding design method and calculate a minimum mean square error (MMSE) filter. Then, the filter is applied to correct the substantially similar blur image. Last, the blur and de-blur images are demonstrated.

Digital image restoration for phase-coded imaging systems

This paper proposes a digital image restoration algorithm for phase-coded imaging systems. In order to extend the depth-of- field (Dof), an imaging system equipped with a properly designed phase-coded lens can achieve an approximately constant point spread function (PSF) for a wide range of depths. In general, a phase-coded imaging system produces blurred intermediate images and requires subsequent restoration processing to generate clear images. For low-computational consumer applications, the kernel size of the restoration filter is a major concern. To fit for practical applications, a pyramid-based restoration algorithm is proposed in which we decompose the intermediate image into the form of Laplacian pyramid and perform restoration over each level individually. This approach provides the flexibility in filter design to maintain manufacturing specification. On the other hand, image noise may seriously degrade the performance of the restored images. To deal with this problem, we propose a Pyramid-Based Adaptive Restoration (PBAR) method, which restores the intermediate image with an adaptive noise suppression module to improve the performance of the phase-coded imaging system for Dof extension.

Image restoration based on multiple PSF information with applications to phase-coded imaging system

Conventional image restoration technique generally uses one point-spread function (PSF) corresponding to an object distance (OD) and a viewing angle (VA) in filter design. However, for those imaging systems, which concern a better balance or a new tradeoff of image restoration within a range of ODs or VAs, the conventional design might be insufficient to give satisfactory results. In this paper, an extension of the minimum mean square error (MMSE) method is proposed. The proposed method defines a cost function as a linear combination of multiple mean square errors (MSEs). Each MSE is for measuring the restoration performance at a specific OD and VA and can be computed from the restored image and its correspondent target image. Since the MSEs for different ODs are lumped into one cost function, the filter solved can provide a better balance in restoration compared with the conventional design. The method is applied to an extended depth-of-field (EDoF) imaging system and computer simulations are performed to verify its effectiveness.

Fidelity tolerance analysis for computational imaging system

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.

Phase coded optics for computational imaging systems

Computational imaging technology can modify the acquisition process to capture extra information at the sensor that can be used for various photographic applications, including imaging with extended depth of field, refocusing photographs after the image is taken or depth extraction for 3D applications. In this paper, we propose a generalized phase coded imaging which involves encoding of the captured light and post-capture decoding for improved features and performance. Phase coded optics utilizes optics to purposely encode specific object information in a more efficient way, which is the most flexible and cost effective solution for correcting optical aberrations or any other optical functions. Practically any shape can be generated on any lens surface for shaping the point spread function of the lens module to achieve desired image results. Phase coded optics is a more general scheme than previous proposed for finding the optimal solutions in digital imaging systems and has proven to be an enabling technology to the imaging problem. Some of the possible applications based on this technique are also investigated in this paper.