This study explores the effectiveness of wavelet analysis techniques on digital holograms of real-world 3D objects.
Stationary and discrete wavelet transform techniques have been applied for noise reduction and compared. Noise
is a common problem in image analysis and successful reduction of noise without degradation of content is
difficult to achieve. These wavelet transform denoising techniques are contrasted with traditional noise reduction
techniques; mean filtering, median filtering, Fourier filtering. The different approaches are compared in terms of
speckle reduction, edge preservation and resolution preservation.
Speckle is an inherent characteristic of coherent imaging systems. Often, as in the case of Ultrasound, Synthetic Aperture
Radar, Laser Imaging and Holography, speckle is a source of noise and degrades the reconstructed image. Various
methods exist for the removal of speckle in such images. One method, which has received attention for the removal of
speckle from coherent imaging, is to use a temporally incoherent source. We create a novel digital signal processing
technique for the reduction of speckle from digital holograms by simulating temporal incoherence during the digital
reconstruction process. The method makes use of the discrete implementation of the Fresnel Transform, which calculates
the reconstructed image for a range of different wavelengths. These different spectral components can be weighted to
suit a temporally incoherent source and the intensities from each wavelength are added together. The method is
examined using the speckle index metric.
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 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.
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.
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.