We propose and investigate a new digital method for the reduction of twin-image noise from digital Fresnel
holograms. For the case of in-line Fresnel holography the unwanted twin is present as a highly corruptive noise
when the object image is numerically reconstructed. We propose to firstly reconstruct the unwanted twin-image
when it is in-focus and in this plane we calculate a segmentation mask that borders this in focus image. The
twin-image is then segmented and removed by simple spatial filtering. The resulting digital wavefield is the
inverse propagated to the desired object image plane. The image is free of the twin-image resulting in improved
quality reconstructions. We demonstrate the segmentation and removal of the unwanted twin-image from in-line
digital holograms containing real-world macroscopic objects. We offer suggestions for its rapid computational
The theorems of Nyquist, Shannon and Whittaker have long held true for sampling optical signals. They showed
that a signal (with finite bandwidth) should be sampled at a rate at least as fast as twice the maximum spatial
frequency of the signal. They proceeded to show how the continuous signal could be reconstructed perfectly
from its well sampled counterpart by convolving a Sinc function with the sampled signal. Recent years have
seen the emergence of a new generalized sampling theorem of which Nyquist Shannon is a special case. This
new theorem suggests that it is possible to sample and reconstruct certain signals at rates much slower than
those predicted by Nyquist-Shannon. One application in which this new theorem is of considerable interest is
Fresnel Holography. A number of papers have recently suggested that the sampling rate for the digital recording
of Fresnel holograms can be relaxed considerably. This may allow the positioning of the object closer to the
camera allowing for a greater numerical aperture and thus an improved range of 3D perspective. In this paper
we: (i) Review generalized sampling for Fresnel propagated signals, (ii) Investigate the effect of the twin image,
always present in recording, on the generalized sampling theorem and (iii) Discuss the effect of finite pixel size
for the first time.
This study investigates segmentation algorithms applicable to digital holography. An assessment of image segmentation tecnhniques applied to intensity images of reconstructions of digital holograms is provided. Digital holography differs from conventional imaging as 3D information is encoded. This allows depth information to be exploited so that focusing of 3D objects, or part there of, at different depths can be achieved. In this paper, segmentation of features is attained in microscopic and macroscopic scenes. We investigate a number
of recently proposed segmentation techniques including (i) depth from focus, (ii) active contours and (iii) hierarchical thresholding. The influence of noise reduction on the segmentation capabilities of each of the techniques on these scenes is demonstrated. For the macrocsopic scenes, each technique is applied before and
after speckle noise reduction is performed using a wavelet based approach. The performance of the segmentation techniques on the intensity information obtained from reconstructed holograms of microscopic scenes is also investigated before and after twin-image reduction has been applied. A comparison of the techniques
and their performances in these circumstances is provided.
Holography allows one to record the amplitude and phase of a wavefront reflected from a three-dimensional (3D)
object, and in principle has advantages for non-contact metrology applications. With digital holography, we use
a digital camera instead of a holographic plate and reconstruct either numerically through simulated propagation
or optically using a spatial light modulator. Digital holograms are in a convenient form for numerical processing
and digital transmission, and recently digital cameras with sucient dynamic range and numbers of pixels have
become available. This has raised interesting problems as to how the 3D data within digital holograms can be
exploited and analysed. We present novel image processing techniques for the recording of wide-angle digital
holograms and, for the purpose of object segmentation, the extraction of object surface profile information from
such digital holograms.
When a digital hologram is reconstructed only points on objects within the depth of focus at the reconstruction
distance are in focus. For complex scenes, scenes containing multiple objects or multiple object features located at
different depths, this can lead to a reconstruction with large blurred regions. Using a depth-from-focus algorithm
we have developed an approach to extract an objects depth information in the form of a depth map from volumes
of reconstructions, where each reconstruction in the volume is a reconstruction at a different focal plane. By
combining the depth map with the volume of reconstructions used to calculate the depth map we can create an
image, an extended focus image, where the full scene is in focus. To our knowledge, this is the first technique
which creates extended focused images of digital holograms encoding macroscopic objects. We present results
for digital holograms containing low and high contrast macroscopic objects.
The nature of digital hologram's allows for the implementation of segmentation process' using volumes of reconstructions
as their input, where each reconstruction in the volume is a reconstruction at a different focal plane.
Our segmentation technique utilizes extracted focus and shape information. In the case of digital hologram's
encoding macroscopic objects, this information is generally obtained using data extraction algorithms applied to
a volume of reconstructions. We have developed a three stage segmentation algorithm for macroscopic objects
encoded in digital hologram's. This algorithm uses a depth-from-focus technique applied to a set of numerical
reconstructions from a single perspective of the digital hologram to extract focus and shape information in the
form of a depth map and a maximum focus map. First we estimate the degree of focus at each coordinate. We
then calculate a depth map of the scene and segment all object coordinates from background coordinates in the
second stage. Finally in the third stage, we perform the segmentation of a digital hologram's reconstruction into
independent objects or object regions. A segmentation image is created through applying histogram and image
processing algorithms to the depth map. We present results of applying our technique to digital hologram's
containing single and multiple macroscopic objects.
In this paper, we analysis the effect of partial occlusions in scenes captured using digital holography. We reconstruct the scene from different perspectives. These reconstructions are then combined, allowing one to overcome foreground occlusions that are obscuring one's view of the scene. The analysis in this paper is carried out with the aid of the Wigner distribution function, allowing us to visualize the energy of the object wavefield and the occluding object wavefield in phase space. We show that by iteratively selecting different views, the original scene can be reconstructed e±ciently. This technique would be useful in situations where transmission of the whole digital hologram, or exhaustive reconstruction of every perspective, was not feasible. We provide results using optically captured digital holograms of real-world objects, and simulated occlusions.
We propose a task-specific digital holographic capture system for three-dimensional scenes, which can reduce the amount of data sent from the camera system to the receiver, and can effectively reconstruct partially occluded objects. The system requires knowledge of the object of interest, but it does not require a priori knowledge of either the occlusion, or the distance the object is from the camera. Subwindows of the camera-plane Fresnel field are digitally propagated to reveal different perspectives of the scene, and these are combined to overcome the unknown foreground occlusions. We demonstrate that careful combination of reconstructions from subwindows can reveal features not apparent in a reconstruction from the whole hologram. We provide results using optically captured digital holograms of real-world objects, and simulated occlusions.
We investigate the application of Independent Component Analysis to the reduction of speckle in reconstructions from digital holograms. Independent Component Analysis computes a linear transformation of a multidimensional distribution that minimizes the statistical dependence between components. It can be seen as an extension of Principal Component Analysis where the transformed bases do not need to be orthonormal. We attempt speckle reduction across multiple hologram reconstructions. A number of situations are investigated,
including recording two holograms over the interval of a day, changing the illumination between two holograms and adding a diffuser in the path of the object beam between subsequent hologram captures. This ensured significant speckle differences between the observations. Results are provided using simulated and optical data.
We report on recent advances made in the area of holographic image processing of three-dimensional (3D) objects. In particular, we look at developments made in the areas of encryption, compression, noise removal, and 3D shape extraction. Results are provided using simulated objects and real-world 3D objects captured using phase- shift digital holography.
We have successfully applied Independent Component Analysis to the removal of background speckle noise from digital holograms. Additive noise removal techniques do not perform well on speckle, which is better characterized as multiplicative noise. In addition, speckle contains 3D information and so cannot be removed completely. We use a blind source separation approach to the reduction of speckle noise in digital holograms. Independent Component Analysis computes a linear transformation of a multi-dimensional distribution that minimizes the statistical dependence between the components. It can be seen as an extension of principal component analysis where the transformed bases do not need to be orthonormal. Although a linear technique, we show how Independent Component Analysis can be applied to the reduction of background speckle in digital holograms. We have captured our digital holograms of three-dimensional objects using phase-shift digital interferometry. In addition, the technique can be extended and applied to segmentation and pattern recognition problems on digital holograms of three-dimensional objects. Results are provided using simulated and optical data.
We present a technique to convert a digital hologram of a three-dimensional (3D) object into a cloud of surface points in 3D space. Two depth-from-defocus techniques are used to generate a depth map for a particular reconstructed perspective of the object encoded in the digital hologram. The Fresnel transform is used to effect defocus, and a histogram-based approach is used to determine the degree of defocus for each neighborhood of pixels. Our experiments involve simulated and real-world objects (captured using phase-shift digital interferometry). The technique could be used in registration and 3D object recognition applications.
One of the principal successes of computer vision over the past thirty years has been the development of robust techniques for the estimation of the structure of a 3D scene given multiple views of that scene. Holography is an established technique for recording and reconstructing real-world 3D objects. A single hologram encodes multiple perspectives of the scene simultaneously, and hence provides a novel avenue of extension of these traditional computer vision techniques. In this paper, we explore the pontential use of digital holograms in 3D scene reconstruction where particular regions of interest are occluded under particular views. In our experiments we employ both synthetic holograms of artificial scenes, and optically-captured digital holograms of real-world objects. We show that by selecting a particular set of perspectives, determined by the occlusions present in the scene, the original scene can be reconstructed.
We report on the results of a study into the characteristics of the blockwise discrete Fourier transform (DFT) coefficients of digital hologram data, with the aim of efficiently compressing the data. We captured digital holograms (whole Fresnel fields) of three-dimensional (3D) objects using phase-shift interferometry. The complex-valued fields were decomposed into nonoverlapping blocks of 8x8 pixels and transformed with the DFT. The inter-block distributions of the 64 Fourier coefficients were analyzed to determine the relative importance of each coefficient. Through techniques of selectively removing coefficients, or groups of coefficients, we were able to trace the relative importance of coefficients throughout a hologram, and over multiple holograms. We used rms error in the reconstructed image to quantify importance in the DFT domain. We have found that the positions of the most important coefficients are common throughout four of the five digital holograms in our test suite. These results will aid us in our aim of creating a general-purpose DFT quantization table that could be universally applied to digital hologram data of 3D objects as part of a JPEG-style compressor.