Biomedical subjects

James N Caron

Publications and source records attributed to James N Caron.

3 recordsLinked to original sources

PubMed2004

Rapid supersampling of multiframe sequences by use of blind deconvolution.

Under certain conditions, multiframe image sequences can be processed to produce images that achieve greater resolution through image registration and increased sampling. This technique, known as supersampling, takes advantage of the spatiotemporal data available in an undersampled 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 upsampled average of the image sequence. This method produces a supersampled image with significantly decreased computational requirements compared with common methods. The method and simulated test results are presented.

Algorithms↗

PubMed2004

Blind deconvolution of audio-frequency signals using the self-deconvolving data restoration algorithm.

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, is applied here to audio-frequency signals showing significant qualitative improvement. Degradation arising from the process of electronic recording and reproduction is significantly reduced.

Acoustics↗

PubMed2002

Noniterative blind data restoration by use of an extracted filter function.

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 noniterative, 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.

Journal Article↗