# Point Spread Function Estimation for a Terahertz Imaging System

- Dan C. Popescu
^{1}Email author and - Andrew D. Hellicar
^{1}

**2010**:575817

https://doi.org/10.1155/2010/575817

© D. C. Popescu and A. D. Hellicar. 2010

**Received: **18 June 2010

**Accepted: **26 August 2010

**Published: **27 September 2010

## Abstract

We present a method for estimating the point spread function of a terahertz imaging system designed to operate in reflection mode. The method is based on imaging phantoms with known geometry, which have patterns with sharp edges at all orientations. The point spread functions are obtained by a deconvolution technique in the Fourier domain. We validate our results by using the estimated point spread functions to deblur several images of natural scenes and by direct comparison with a point source response. The estimations turn out to be robust and produce consistent deblurring quality over the entire depth of the focal region of the imaging system.

## Keywords

## 1. Introduction

Imaging systems operating in the terahertz (THz) region of the spectrum have the potential to enable new applications due to the unique combination of properties that occur in this region, such as penetration through clothes, packaging and plastics, and also the fact that THz waves are nonionising and hence do not pose a health hazard for humans. Application domains such as security [1], medical imaging [2] and nondestructive testing [3] are likely to benefit from developments in this area. Despite these advantages, commercial systems in this spectral region have been slow to emerge, due to a lack of mature THz components and technology.

Imaging at THz frequencies poses a challenge to the resolution of the images that can be achieved [4], both because of the technology's immaturity and the long wavelengths employed (relative to wavelengths at optical frequencies), which are typically around or over the millimetre range. Due to the expensive nature of terahertz imaging systems and the likelihood of long acquisition times, there is ample scope for employing image processing techniques, without increasing the system cost nor image acquisition time. Knowledge of the point spread function (PSF) of the imaging system is very important for improving image quality.

The point spread function is the imaging system's response to an ideal, point-like source. In practical situations it may not be easy to find such ideal sources, and methods relying on direct measurement of the point spread function from the response of a point-source approximation will face the challenge of balancing resolution against sensitivity. Examples of approximations for point sources include standard stars or quasars when calibrating astronomical instruments [5, 6], recording beads in microscopy [7, 8], and pinholes into opaque materials for various optical systems [9]. However, most practical computational methods used for the estimation of the point spread function are indirect and rely on some measured output of the system and possibly some additional knowledge of the scene being imaged and imaging system parameters. In general, these methods are application dependent and fall into two categories: parametric and nonparametric methods. Parametric methods assume that the PSF belongs to a given shape class, modelled by a small number of parameters, such as a confusion disk or a Gaussian, and then focus on finding a robust method for estimating the parameters [10–13]. Nonparametric methods [14, 15] allow for the point spread function to be of any shape although they may still impose some mild restrictions on it, such as not having a too large a support.

Here we propose an approach for calculating the PSF based on the imaging of phantom objects designed to take advantage of the imaging system characteristics. Before explaining this approach, the system design will be discussed along with properties of the PSF. The paper is organised as follows. In Section 2 we present the architecture of our experimental terahertz imaging system. In Section 3 we describe the phantoms used in our experiments and the alignment procedure. We present our PSF estimation procedure and experimental results in Section 4 and summarise our conclusions in Section 5.

## 2. System Design

In the CSIRO Wireless Technologies Laboratory, we have designed a 180 GHz coherent imaging system. The system operates in reflection mode. A reflection mode approach is required where the object being imaged does not allow THz waves to penetrate through the object whereas THz waves being scattered off the object may be detected. Practical scenarios requiring this approach include detection of skin lesions and cancers, explosive detection in packaging, and corrosion detection under paint.

The focused THz beam on the target surface is created by a quasioptical system that directs a THz beam generated from a THz source. The THz source employs a smooth-walled spline-profile horn to create a diverging Gaussian beam. This beam then strikes mirror M1 which collimates the beam. The collimated beam exhibits a Gaussian amplitude distribution and a constant phase distribution in the plane orthogonal to the direction of beam propagation. The beam's amplitude cross-section does not vary between mirrors M1 and M2. The collimated beam strikes mirror M2 and is transformed into a Gaussian beam converging towards the focal point on the target surface. A portion of the THz beam penetrates the target, and the remainder is reflected across a range of angles. The energy reflected off the target and captured by mirror M2 is coupled through the optical system back to mirror M1 and is focused towards the THz source. A silicon wafer partially reflects the energy into a THz receiver.

The amplitude of the signal captured at the receiver is used to determine the amount of energy reflected by the target. The THz source is a continuous sine wave oscillating at 180 GHz. Reflection at the target generates a reflected wave which differs from the incident sine wave in both amplitude and phase. Measurement of the phase proceeds by comparing the phase of the signal at the THz receiver with the phase of the THz source. The schematic diagram in Figure 1(c) shows the electronics that achieves this comparison. A 10 dB coupler is used to capture 10% of the transmitted signal. This signal is mixed down to an IF frequency of approximately 1.9 GHz. The signal at the THz receiver is also mixed down to 1.9 GHz. The two 1.9 GHz signals are then filtered and cross-correlated to determine the phase difference. The described system is physically large, occupying a region of approximately 1 m 1 m. However, the system is based on electronic components which have the potential to be reconfigured in the future into a compact configuration.

The described system has a PSF that ideally should be Gaussian, have flat phase, and be invariant to the image coordinates. Invariance to image coordinates follows as the PSF does not vary as the target is translated through the fixed beam. The depth of focus and PSF size can be calculated from the properties of the source and receiver horns, which are similar to those described in [16], and the focal lengths of the mirrors M1 and M2. The resulting depth of focus is about 3.4 mm with a spot size of 2.5 mm.

## 3. Phantom Design

### 3.1. Phantom Image Registration

## 4. Point Spread Function Estimation

This approximation is particularly appropriate in the case of white Gaussian noise and a flat signal spectrum. In the case of reflections from a flat metal phantom, the spectrum is flat by design. Measurements of signal reflectance with no target have indeed confirmed that the noise in our system closely resembles white Gaussian noise [17]. Therefore, the simplified Wiener filter model of (4) is very suitable for our PSF estimation.

### 4.1. Experimental Setup

We remark that the phase of the point spread function is almost flat over the high intensity region of the PSF signal, which is in accordance with the phase variation of a Gaussian beam in its focal region. This is an expected result, since our source and receiver horns were designed to produce Gaussian beams.

### 4.2. Validation

The effects of the deblurring are most noticeable on the amplitude images of Figure 6. We remark the poor quality of the deblurring obtained using the PSF estimated directly from the pinball response. Admittedly, a ball with diameter around 2 mm is not a close match to an ideal point source, but it is, in practical terms, as close as we could get to it; attemps to use metal balls of smaller sizes in our experiments have resulted in response signals too weak for any reliable estimation of their shape. By contrast, both deblurred images using the PSFs estimated from phantom data show similar and remarkable detail improvement. On the coin image, the edges are sharper. Details in the area of the mouth and nose are enhanced and some texture areas of the hair become more prominent. The letters on the coin remain indistinguishable, which is to be expected, because their fine features have sizes smaller than the wavelength. However, the overall letters' blocks are still more visible on the two deblurred images. A tiny horizontal image misalignment, due to a mechanical lag of the translation table, also becomes visible in the deblurred images, especially across the upper half of the coin. The features on the kangaroo keyring are enhanced, particularly around the head and the paws areas, where the edges become better separated. The two PSFs estimated from the two phantom images are not identical, which is to be expected, given the fact that they are estimated at slightly different depths. In spite of this, the deblurring results on Figure 6(c) are remarkably similar. The patterns in the phase images, shown in Figure 7(b), are more consistent with depth geometry suggested by the optical image in Figure 7(a). By contrast, the phase image at the right of Figure 7(a) (corresponding to deblurring with the pinball PSF) is again the most inconsistent with the same depth geometry pattern.

## 5. Conclusions

We have presented a procedure for estimating the complex-valued point spread function of a terahertz imaging system which operates in reflection mode. A metal phantom with known geometry is placed at the focal region of the system and imaged. From this acquired phantom image and a computed version of the ideal phantom image, registered to the acquired image, the point spread function is estimated using a Wiener filter technique. The quality of the estimated point spread function is tested by using it to deconvolve images of a scene containing manufactured objects with good reflective properties. The improvement in detail areas of the scene validates the point spread function estimation obtained using our technique. Our methodology was applied in a context where there was no practical alternative for estimating the PSF using direct measurement of a point source response. The only physical approximations of point sources for which we could obtain reasonably strong response signals were metal balls too large in size, and the PSFs estimated from such direct measurements turned out to be of poor quality. Estimations of the PSF at slightly different depths around the focal plane have produced deblurring of similar quality at all depths around the focal plane region. While in our experiments we have only tested a THz imaging system in reflection mode, the same technique could be applied for a THz imaging system operating in transmission mode; the only challenge would be the manufacturing of a phantom from materials with precisely controlled absorption coefficients. A study of such materials is presented in [20].

## Declarations

### Acknowledgment

The authors acknowledge Carl Holmesby for manufacturing the phantoms used in our experiments.

## Authors’ Affiliations

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