A method of implement FFT based on FPGA IP Core is introduced in this paper. In addition, for the spectrum leakage caused by the truncation of the non-integer-period sampling, an improved method of adding window to the input signal to restrain the spectrum leakage is proposed. The design was simulated in the Matlab environment. The results show that the proposed method has good performance with some improvement.
For the real-time motion deblurring, it is of utmost importance to get a higher processing speed with about the same image quality. This paper presents a fast Richardson-Lucy motion deblurring approach to remove motion blur which rotates blurred image under blurring paths. Hence, the computational time is reduced sharply by using one-dimensional Fast Fourier Transform in one-dimensional Richardson-Lucy method. In order to obtain accurate transformational results, interpolation method is incorporated to fetch the gray values. Experiment results demonstrate that the proposed approach is efficient and effective to reduce motion blur under the blur paths.
Blind image deblurring is an important issue. In this paper, we focus on solving this issue by constrained regularization method. Motivated by the importance of edges to visual perception, the edge-enhancing indicator is introduced to constrain the total variation regularization, and the bilateral filter is used for edge-preserving smoothing. The proposed edge enhancing regularization method aims to smooth preferably within each region and preserve edges. Experiments on simulated and real motion blurred images show that the proposed method is competitive with recent state-of-the-art total variation methods.
We present a method to extract edges using zero-crossing feature and contour measure. This method differs markedly from previous ones, since it provided a means of quantitative analysis to detect zero-crossing. There are two main steps in this method. Firstly, the edge intensity was obtained through the value of contour measure. Secondly, the actual edges are identified according to the edges intensity. A series of experiments are performed to test the algorithm proposed, which show that the edges is extracted more accurately and completely.