We consider density deconvolution with zero-mean Laplace noise in the context of an error component regression model. adapt minimax methods Meister (2006) to allow estimation unknown variance. propose a semi-uniformly consistent estimator for ordinary-smooth target and modified "variance truncation device” provide simulation study practical guidance choice smoothness parameters density. apply r...

Having attractiveness in digital cameras, the digital image processing is getting more imperative nowadays. One of the most common problems facing with digital photography is noise and blurring that needs restoration. In this paper, we present a new method for image blind deconvolution [2]. The Proposed Method employs threshold based image restoration technique in blind image deconvolution. The...

We present a new speech enhancement algorithm in a car environment with two microphones. The car audio signals and other background noises are the target noises to be suppressed. Our algorithm is composed of two main parts, i.e., the spatial and the temporal processes. The multi-channel blind deconvolution (MBD) is applied to the spatial process while the Kalman filter with a second-order high ...

We adress the problem of spherical deconvolution in a non parametric statistical framework, where both the signal and the operator kernel are subject to error measurements. After a preliminary treatment of the kernel, we apply a thresholding procedure to the signal in a second generation wavelet basis. Under standard assumptions on the kernel, we study the theoritical performance of the resulti...

If the Navier-Stokes equations are averaged with a local, spacial convolution type filter, φ = gδ∗φ, the resulting system is not closed due to the filtered nonlinear term uu. An approximate deconvolution operator D is a bounded linear operator which satisfies u = D(u) + O(δ), where δ is the filter width and α ≥ 2. Using a deconvolution operator as an approximate filter inverse, yields the closu...

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate a...

In performing blind deconvolution to remove reverberation from speech signal, most acoustic deconvolution filters need a great many number of taps, and acoustic environments are often time-varying. Therefore, deconvolution filter coefficients should find their desired values with limited data, but conventional methods need lots of data to converge the coefficients. In this paper, we use sparse ...