A deconvolution error avoidance technique in Richardson-Lucy deconvolution (RLdeconv) is proposed, which is used for inversely analysing the SRAM margin variations caused by the Random Telegraph Noise (RTN). The proposed technique reduces the phase difference between the deconvoluted RTN distribution and feedback-gain in the maximum likelihood (MLE) gradient iteration cycles. This avoids an unw...

We present an approach to determine su cient conditions for the global convergence of iterative blind deconvolution algorithms using nite impulse response (FIR) deconvolution lters. The novel technique, which incorporates Lyapunov's direct method, is general, exible and can be easily adapted to analyze the behaviour of many types of nonlinear iterative signal processing algorithms. Speci cally,...

In the following paper we investigate two algorithms for blind signal deconvolution that has been proposed in the literature. We derive a clear interpretation of the information theoretic objective function in terms of signal processing and show that only one is appropriate to solve the deconvolution problem, while the other will only work if the unknown filter is constrained to be minimum phas...

Deconvolution of the telescope Point Spread Function (PSF) is necessary for even moderate dynamic range imaging with interferometric telescopes. The process of deconvolution can be treated as a search for a model image such that the residual image is consistent with the noise model. For any search algorithm, a parameterized function representing the model such that it fundamentally separates si...

Operationally, index functions of variable Hilbert scales can be viewed as generators for families of spaces and norms and, thereby, associated scales of interpolatory inequalities. Using one parameter families of index functions based on the dilations of given index functions, new classes of interpolatory inequalities, dilational interpolatory inequalities (DII), are constructed. They have ord...

Compressive Sensing Deconvolution (CS-Deconvolution) is a new challenge problem encountered in a wide variety of image processing fields. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly sparse represented by curvelet frame system; then the curvelet coefficients are encoded by a structurally random matrix based CS sampling technique. In order to improve t...

The blind deconvolution of ultrasound sequences in medical ultrasound technique is still a major problem despite the efforts made. This paper presents a blind noninverse deconvolution algorithm to eliminate the blurring effect, using the envelope of the acquired radio-frequency sequences and a priori Laplacian distribution for deconvolved signal. The algorithm is executed in two steps. Firstly,...