A Novel Image Restoration Method based on Iterative Algorithm

In the paper, we proposed a new iterative algorithm and use a entirely new iterative factor. Firstly, we adopt the Exp function in the iterative factor, because we want each iterative result preserves the nonnegative constraint; Secondly, we make the iterative factor in a reciprocal form ,this way can produce two advantages, one is we can get a more stable and continuous results after each iteration; the other is we can achieve this algorithm in hardware more convenient. Thirdly, we add a low-pass filter and the edge of the scale in the iterative factor, this way we can get a better result, the image SNR is higher and the MSE is lower. Meanwhile for the image sequence, we adopt the two-step iterative algorithm. The result shows the algorithm own the faster convergence speed and the better convergence result. Different from the other algorithm for blind restoration, although we should select the parameter in the starting of the algorithm, the algorithm doesn’t sensitive for the parameter. So the algorithm possesses very strong adaptability for the blind image deblurring. So a novel algorithm based on an iterative and nonnegative algorithm was proposed to perform blind deconvolution.

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Temporal Processing Across Multiple Topographic Maps in the Electrosensory System

Journal of Neurophysiology ◽

10.1152/jn.90300.2008 ◽

2008 ◽

Vol 100 (2) ◽

pp. 852-867 ◽

Cited By ~ 69

Author(s):

Rüdiger Krahe ◽

Joseph Bastian ◽

Maurice J. Chacron

Keyword(s):

Frequency Tuning ◽

Phase Locking ◽

The Body ◽

The Other ◽

Stimulus Class ◽

Cellular Mechanisms ◽

Pass Filter ◽

Low Pass Filter ◽

Sensory Stimuli ◽

Low Pass

Multiple topographic representations of sensory space are common in the nervous system and presumably allow organisms to separately process particular features of incoming sensory stimuli that vary widely in their attributes. We compared the response properties of sensory neurons within three maps of the body surface that are arranged strictly in parallel to two classes of stimuli that mimic prey and conspecifics, respectively. We used information-theoretic approaches and measures of phase locking to quantify neuronal responses. Our results show that frequency tuning in one of the three maps does not depend on stimulus class. This map acts as a low-pass filter under both conditions. A previously described stimulus-class-dependent switch in frequency tuning is shown to occur in the other two maps. Only a fraction of the information encoded by all neurons could be recovered through a linear decoder. Particularly striking were low-pass neurons the information of which in the high-frequency range could not be decoded linearly. We then explored whether intrinsic cellular mechanisms could partially account for the differences in frequency tuning across maps. Injection of a Ca2+ chelator had no effect in the map with low-pass characteristics. However, injection of the same Ca2+ chelator in the other two maps switched the tuning of neurons from band-pass/high-pass to low-pass. These results show that Ca2+-dependent processes play an important part in determining the functional roles of different sensory maps and thus shed light on the evolution of this important feature of the vertebrate brain.

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Consonant Confusions Associated with Hearing Loss above 2000 Hz

Journal of Speech and Hearing Research ◽

10.1044/jshr.1704.669 ◽

1974 ◽

Vol 17 (4) ◽

pp. 669-681 ◽

Cited By ~ 19

Author(s):

Annabelle E. Sher ◽

Elmer Owens

Keyword(s):

Normal Hearing ◽

Initial Position ◽

The Other ◽

Final Position ◽

High Tone ◽

Pass Filter ◽

Low Pass Filter ◽

Speech Stimuli ◽

Low Pass ◽

Manner Of Articulation

A 100-item test for the identification of phonemes was presented to two groups of listeners. One group consisted of 35 patients with normal hearing up to 2000 Hz accompanied by a high-tone loss. The other group consisted of persons with normal hearing who heard the speech stimuli presented through a low-pass filter with a cutoff at 2040 Hz. There were no significant differences between the two groups in overall scores, in probabilities of error for individual phonemes, or in the kinds of error substitutions made. Findings were as follows: (1) the overall scores indicated difficulty in phonemic identification; (2) the phonemes contributing to this difficulty were primarily /b, p, t, k, s, θ/ in both the initial and final positions, /t∫, ∫, f, dƷ, z, v/ in the final position only, and /d/ in the initial position only; and (3) the phonemes substituted for the stimulus phonemes in the initial position were the same in manner of articulation as the stimulus phoneme, and typically only one confusion occurred per stimulus phoneme. The phonemes substituted for the stimulus phonemes in the final position were not necessarily the same in manner of articulation as the stimulus phoneme, and more than one confusion generally occurred for each stimulus phoneme.

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Improvement of tomographic reconstruction in bone SPECT

Nuklearmedizin ◽

10.1055/s-0038-1623932 ◽

2006 ◽

Vol 45 (01) ◽

pp. 35-40 ◽

Cited By ~ 1

Author(s):

G. Heidrich ◽

C.O. Sahlmann ◽

U. Siefker ◽

H. Luig ◽

C. Werner ◽

...

Keyword(s):

Iterative Algorithm ◽

Iterative Reconstruction ◽

Wiener Filter ◽

Filtered Backprojection ◽

Maximal Interval ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Bone Spect

Summary Aim: The comparison between iterative reconstruction and filtered backprojection in the reconstruction of bone SPECT in the diagnosis of skeletal metastases. Patients, methods: 47 consecutive patients (vertebral segments: n = 435), with suspected malignancy of the vertebral column, were examined by bone scintigraphy and MRI (maximal interval between the two procedures ± 5 weeks). The SPECT-data were reconstructed with an iterative algorithm (ISA) and with filtered backprojection. We defined semiquantitative criteria in order to assess the quality of the tomograms. Conventional reconstruction was performed both by a Wiener-filter and a low-pass-filter. Iterative reconstruction was performed by the ISA algorithm. The clinical evaluation of the different reconstruction algorithms was performed by MRI as the gold-standard. Results: Sensitivity (%): 87.3 (ISA), 86.4 (low-pass), 79.7 (Wiener); specificity (%): 95.3 (ISA), 95 (low-pass), 85.4 (Wiener). The sensitivity of iterative reconstructed SPECT and low-pass reconstructed SPECT was significantly higher (p <0.05) compared with the sensitivity of SPECT reconstructed by the Wiener-filter. The specificity of iterative reconstruction ISA and low-pass-filter reconstructed SPECT were significantly higher compared with the SPECT data reconstructed by the Wiener-filter. ISA was significantly superior to the Wiener- SPECT relating to all criteria of quality. Iterative reconstruction was significantly superior to the low-pass-SPECT relating to 2 of 3 criteria. In addition the Wiener-SPECT was significantly inferior to the low-pass-SPECT regarding to 2 of 3 criteria. Conclusion: In our series the iterative algorithm ISA was the method of choice in the reconstruction of bone SPECT data. In comparison with conventional algorithms ISA offers a significantly higher quality of the tomograms and yields a high diagnostic accuracy.

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CONVERGENCE OF A CONVOLUTION-FILTERING-BASED ALGORITHM FOR EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis ◽

10.1142/s1793536909000205 ◽

2009 ◽

Vol 01 (04) ◽

pp. 561-571 ◽

Cited By ~ 11

Author(s):

CHAO HUANG ◽

LIHUA YANG ◽

YANG WANG

Keyword(s):

Empirical Mode Decomposition ◽

Iterative Algorithm ◽

Moving Average ◽

Limit Function ◽

Continuous Variables ◽

Pass Filter ◽

Low Pass Filter ◽

Mode Decomposition ◽

Low Pass ◽

Convolution Filters

Lin et al. propose the iterative Toeplitz filters algorithm as an alternative iterative algorithm for Empirical Mode Decomposition (EMD). In this alternative algorithm, the average of the upper and lower envelopes is replaced by certain "moving average" obtained through a low-pass filter. Performing the traditional sifting algorithm with such moving averages is equivalent to iterating certain convolution filters (finite length Toeplitz filters). This paper studies the convergence of this algorithm for signals of continuous variables, and proves that the limit function of this iterative algorithm is an ideal high-pass filtering process.

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Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling Permutation Entropy

Entropy ◽

10.3390/e23010030 ◽

2020 ◽

Vol 23 (1) ◽

pp. 30

Author(s):

Antonio Dávalos ◽

Meryem Jabloun ◽

Philippe Ravier ◽

Olivier Buttelli

Keyword(s):

Theoretical Perspective ◽

Original Method ◽

The Other ◽

Multiscale Entropy ◽

Permutation Entropy ◽

Pass Filter ◽

Low Pass Filter ◽

Theoretical Predictions ◽

Low Pass ◽

The Difference

Multiscale Permutation Entropy (MPE) analysis is a powerful ordinal tool in the measurement of information content of time series. MPE refinements, such as Composite MPE (cMPE) and Refined Composite MPE (rcMPE), greatly increase the precision of the entropy estimation by modifying the original method. Nonetheless, these techniques have only been proposed as algorithms, and are yet to be described from the theoretical perspective. Therefore, the purpose of this article is two-fold. First, we develop the statistical theory behind cMPE and rcMPE. Second, we propose an alternative method, Refined Composite Downsampling Permutation Entropy (rcDPE) to further increase the entropy estimation’s precision. Although cMPE and rcMPE outperform MPE when applied on uncorrelated noise, the results are higher than our predictions due to inherent redundancies found in the composite algorithms. The rcDPE method, on the other hand, not only conforms to our theoretical predictions, but also greatly improves over the other methods, showing the smallest bias and variance. By using MPE, rcMPE and rcDPE to classify faults in bearing vibration signals, rcDPE outperforms the multiscaling methods, enhancing the difference between faulty and non-faulty signals, provided we apply a proper anti-aliasing low-pass filter at each time scale.

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