Abstract: Midpoint filter is quite effective in recovering the
images confounded by the short-tailed (uniform) noise. It, however,
performs poorly in the presence of additive long-tailed (impulse)
noise and it does not preserve the edge structures of the image
signals. Median smoother discards outliers (impulses) effectively, but
it fails to provide adequate smoothing for images corrupted with nonimpulse
noise. In this paper, two nonlinear techniques for image
filtering, namely, New Filter I and New Filter II are proposed based
on a nonlinear high-pass filter algorithm. New Filter I is constructed
using a midpoint filter, a highpass filter and a combiner. It suppresses
uniform noise quite well. New Filter II is configured using an alpha
trimmed midpoint filter, a median smoother of window size 3x3, the
high pass filter and the combiner. It is robust against impulse noise
and attenuates uniform noise satisfactorily. Both the filters are shown
to exhibit good response at the image boundaries (edges). The
proposed filters are evaluated for their performance on a test image
and the results obtained are included.
Abstract: In this paper, we propose a new class of Volterra series based filters for image enhancement and restoration. Generally the linear filters reduce the noise and cause blurring at the edges. Some nonlinear filters based on median operator or rank operator deal with only impulse noise and fail to cancel the most common Gaussian distributed noise. A class of second order Volterra filters is proposed to optimize the trade-off between noise removal and edge preservation. In this paper, we consider both the Gaussian and mixed Gaussian-impulse noise to test the robustness of the filter. Image enhancement and restoration results using the proposed Volterra filter are found to be superior to those obtained with standard linear and nonlinear filters.
Abstract: Linear convolutive filters are fast in calculation and in application, and thus, often used for real-time processing of continuous data streams. In the case of transient signals, a filter has not only to detect the presence of a specific waveform, but to estimate its arrival time as well. In this study, a measure is presented which indicates the performance of detectors in achieving both of these tasks simultaneously. Furthermore, a new sub-class of linear filters within the class of filters which minimize the quadratic response is proposed. The proposed filters are more flexible than the existing ones, like the adaptive matched filter or the minimum power distortionless response beamformer, and prove to be superior with respect to that measure in certain settings. Simulations of a real-time scenario confirm the advantage of these filters as well as the usefulness of the performance measure.
Abstract: Micro electromechanical sensors (MEMS) play a vital
role along with global positioning devices in navigation of
autonomous vehicles .These sensors are low cost ,easily available but
depict colored noises and unpredictable discontinuities .Conventional
filters like Kalman filters and Sigma point filters are not able to cope
with nonwhite noises. This research has utilized H∞ filter in nonlinear
frame work both with Kalman filter and Unscented filter for
navigation and self alignment of an airborne vehicle. The system is
simulated for colored noises and discontinuities and results are
compared with not robust nonlinear filters. The results are found
40%-70% more robust against colored noises and discontinuities.
Abstract: Median filters with larger windows offer greater smoothing and are more robust than the median filters of smaller windows. However, the larger median smoothers (the median filters with the larger windows) fail to track low order polynomial trends in the signals. Due to this, constant regions are produced at the signal corners, leading to the loss of fine details. In this paper, an algorithm, which combines the ability of the 3-point median smoother in preserving the low order polynomial trends and the superior noise filtering characteristics of the larger median smoother, is introduced. The proposed algorithm (called the combiner algorithm in this paper) is evaluated for its performance on a test image corrupted with different types of noise and the results obtained are included.