Quarktet SeDDaRA Abstracts

W.E. Vanderlinde and J.N. Caron, "Blind Deconvolution of SEM Images," Proceedings of the 33rd International Symposium for Testing and Failure Analysis, November, 2007, p. 97-102.

Blind deconvolution techniques were used to enhance scanning electron microscope (SEM) images in the range of 200,000x to 500,000x magnification. Typical SEM samples were imaged including a gold island reference standard, a plams delayered integrated circuit, and an integrated circuit cross section. Image resolution improvement up to 40% was observed. However, it was necessary to use 16-bit images with great than 120:1 signal to noise ratio, which required 10 minute frame times.

J.N. Caron, "Blind deconvolution of audio-frequency signals using the self-deconvolving data restoration algorithm," Journal of the Acoustical Society of America, Vol. 116, Issue 1, pp. 373-378, 2004.

A signal processing algorithm has been developed in which a filter function is extracted from degraded data through mathematical operations. The filter function can be used to restore much of the degraded content of the data through use of a deconvolution process. The operation can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated Self-deconvolving Data Reconstruction Algorithm (SeDDaRA), is applied here to audio-frequency signals showing significant qualitative improvement. Degradation arising from the process of electronic recording and reproduction is significantly reduced.

J.N. Caron, N.M. Namazi, and C.J. Rollins, "Noniterative blind data restoration by use of an extracted filter function," Applied Optics, November 10, 2002, Vol 41, No. 32, p. 6884.

A signal-processing algorithm has been developed where a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of a deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated self-deconvolving data reconstruction algorithm, has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is non-iterative, computationally efficient, and requires little user input. Implementation is straightforward, allowing inclusion into many types of signal-processing software and hardware. The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method assumes the power law is frequency dependent. The function derived comparing the frequency spectrum of the degraded data with the spectrum of a signal with the desired frequency response. The second method assumes this function is a constant of frequency. This approach requires little knowledge of the original data or the degradation.

J.N. Caron, "Rapid supersampling of multiframe sequences by use of blind deconvolution," Optics Letters, September, 2004, Vol 29, No. 17, p. 1986-1988.

Under certain conditions, multi-frame image sequences can be processed to produce images that achieve greater resolution through image registration and increased sampling. This technique, known as super-sampling, takes advantage of the spatial-temporal data available in an under-sampled imaging sequence. In this effort, the image registration is replaced by application of a fast blind deconvolution technique to remove the motion blur in the up-sampled average of the image sequence. This produces a super-sampled image with significantly decreased computational requirements compared to common methods. Method and simulated test results are presented.

J.N. Caron, N.M. Namazi, R.L. Lucke, C.J. Rollins, and P.R. Lynn, Jr., "Blind data restoration with an extracted filter function," Optics Letters , August 1, 2001, Vol 26, No.15, p. 1164.

A method for performing blind deconvolutions on degraded images and data has been developed. The technique uses a power law relation applied to the Fourier transform of the degraded data to extract a filter function. This filter function closely resembles the point spread function of the system, and can be used to restore and enhance higher-frequency content. The process is non-iterative and requires only that the point spread function be space-invariant and the transfer function is real. The algorithm has been validated by direct comparisons using a pseudo-inverse filter with known transfer functions.