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.
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.
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.
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.
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.
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.