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Next: Combining Undersampled Dithered Data - A Review of the Options
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Table of Contents - Subject Index - Author Index - Search - PS reprint - PDF reprint Devillard, N. 1999, in ASP Conf. Ser., Vol. 172, Astronomical Data Analysis Software and Systems VIII, eds. D. M. Mehringer, R. L. Plante, & D. A. Roberts (San Francisco: ASP), 333

Infrared Jitter Imaging Data Reduction: Algorithms and Implementation

Nicolas Devillard
European Southern Observatory, Munich, Germany

Abstract:

Jitter imaging (also known as microscanning) is probably one of the most efficient ways to perform astronomical observations in the infrared. It requires very efficient filtering and recentering methods to produce the best possible output from raw data. This paper discusses issues attached to Poisson offset generation, efficient infrared sky filtering, offset recovery between planes through cross-correlation and/or point pattern recognition techniques, plane shifting with subpixel resolution through various kernel-based interpolation schemes, and 3D filtering for plane accumulation. Several algorithms are described for each step, having in mind an automatic data processing in pipeline mode (i.e., without user interaction) as intended for the Very Large Telescope. Implementation of these algorithms in optimized ANSI C (the eclipse library) is also described here.

1. Introduction

Jitter imaging is an efficient method used in astronomical infrared observations to take care of sky background issues with a minimum loss of observing time. By observing the same spot on the sky with small offsets around a central position for each exposure, it is hopefully possible to deduce the sky background variations directly by filtering the images, and to separate the astronomical and the sky signals.

A typical acquisition in jitter mode consists in a set of 10 to 100 frames. The first frame is assumed to be centered on the point of interest; the following frames are slightly offset from the first position with offsets no larger than a reasonably small fraction of the detector size (15% for SOFI/ISAAC). The data reduction process consists of sky estimation and subtraction, plane recentering, and finally frame co-addition to a single frame. Each processing step is discussed here in the context of automatic data reduction in pipeline mode (i.e., without user interaction).

2. Poisson Offset Generation

The following procedure is used to generate homogeneous offsets to use for a standard jittered observation. Points are generated with a Poisson sampling law (Wolberg 1990) which specifies that no two points are closer to each other than a minimal distance. This distance is computed by taking into account the size of the (rectangular) domain in which points are generated, and the number of points to generate. The minimal distance between any two points for a set of n points generated in a rectangle of size S_x x S_y is chosen as:

$$\sqrt{ \frac{S_x S_y}{n\sqrt{ 2 } } }$$ d(S_x,S_y,n) = (1)

3. Sky Background Filtering

Sky background filtering is done through a method called sky combination. The method is an iteration on each pixel position on the detector along a time line. The algorithm can be described for each pixel position as:

• Loop on all planes. For each plane, do the following:
• Loop on all pixel positions on the detector. For each position, do the following:
• Extract an array of $\pm h$ pixel values in time around the current pixel (this produces 2h+1 values).
• Scale (divide) each pixel in the array by the (filtered) average value of the plane from which it was extracted.
• Sort this scaled array by increasing pixel value.
• Reject the rmin and rmax pixel values in the sorted array.
• Average the remaining pixel values and assign the result to the same pixel position in the same current plane in the output cube.

Notice that if rmin = rmax = h, this method is equivalent to a running median filter; only the central sorted value is kept.

4. Frame Offset Detection

The next step is to measure or acquire the offsets between the frames. It is reasonable to assume that the acquisition orders for telescope pointing are available as ancillary data. If they are not, there should be an automatic way of finding these offsets with a minimal chance of error. Lastly, users may want to use their own method to detect offsets and feed their input into the algorithm for ultimate refining. We discuss here a cross-correlation method used both for automatic offset detection and refining up to subpixel resolution.

4.1 Points of Interest

Cross-correlation techniques need valid points in the image to work properly. We describe here a simple peak detector used to detect such points. In most cases, it should provide a reasonable number of points to use for offset detection.

Create a binary image from the input frame by thresholding with a $K\sigma$ clipping. Instead of using mean and sigma values for this clipping, we estimate these values with a median and average absolute deviation from the median, to be less sensitive to noise.

The value of K depends on the amount of noise and the number of objects present in the image. For very noisy images, it is best to lower K to a value between 0.1 and 1 (the default is 3).

This thresholding creates a crude map of bright regions in the input image. To clean bad pixels and other such small defects, a binary morphological erosion is then performed. A classical floodfill algorithm is then applied to estimate the centroid of each remaining region, weighted by pixel values of the input image. A list of detected object centers and maximum pixel values in each region is then returned.

4.2 Cross-correlation product

The cross-correlation of two images I₁ and I₂ is defined as the product:

$$\sum_{p_1 \in w_1}{\sum_{p_2 \in w_2}{p_1 \otimes p_2}}$$ (2)

where p₁ is the pixel value which index is running over the domain of interest w₁ in the image I₁, and similarly p₂ a pixel value which 2-dimensional index is running over the domain of interest w₂ in the image I₂. The cross-correlation product we use is a sum of squared differences:

$$p_1 \otimes p_2 = \sum_{w_1,w_2}{(p_1 - p_2)^2}$$ (3)

With this criterion, the point of greatest similarity corresponds to a minimum of difference in the cross-correlation product. A subpixel offset detection is then applied by fitting a parabola to the cross-correlation signal in x and y, and looking for the subpixel minimum. This method is theoretically precise up to a 1/100 pixel precision in the ideal case of noiseless images. The precision obtained with SOFI data is about 1/10 pixel.

In automatic mode when there is no indication of what the offsets between frames are, this cross-correlation technique is used within a wide search zone. When a first estimate of the offsets is available (refining mode), the search area is lowered to a few pixels in all directions to speed up the process.

5. Frame Registration

To shift an image by a subpixel offset, it is necessary to use resampling methods. Given some assumptions about the input signal cut-off frequency, an image is considered as the discretization of a continuous band-limited signal. According to Shannon's theorem, it is possible to completely retrieve this continuous signal given enough samples. It is then easy to resample the image, i.e., to construct a new image with samples taken at a new position of interest, namely the required offset. Interested readers are referred to Wolberg (1990).

In the present implementation, we chose to resample images in the image space. The interpolation kernel is based on the 16 closest pixel values, and the kernel definition is based on a hyperbolic tangent function in Fourier space:

$$H_k(f) = \Biggl(\frac{tanh(k(x+0.5))+1}{2}\Biggr)\Biggl(\frac{tanh(k(-x+0.5))+1}{2}\Biggr)$$ (4)

This interpolation kernel is then computed in image space through a Fourier transform. It converges exponentially toward zero, which makes it an ideal candidate for resampling with a small pixel neighborhood.

6. 3D Averaging

The last step of the reduction is to concatenate all registered planes on the third axis into a single plane. The method we use is a filtered 3D average. For each pixel position, we reject outliers along the time axis and average remaining pixels to a single value. This yields the single output frame, which is the result of the data reduction process.

7. Conclusion

Interested readers are referred to a more complete ESO document at http://www.eso.org/projects/dfs/papers/jitter98/. The eclipse library has been used to implement this algorithm. The home page for this software package can be found at http://www.eso.org/eclipse.

References

Wolberg, G. 1990, Digital Image Warping, (Los Alamitos: IEEE Press)

© Copyright 1999 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA Next: Combining Undersampled Dithered Data - A Review of the Options
Up: Data Analysis and Processing Techniques
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