In this paper, we present a new frequency identification technique using the recent methodology of compressive sensing and discrete prolate spheroidal sequences with optimal energy concentration. Using the bandpass form of discrete prolate spheroidal sequences as basis matrix in compressive sensing, compressive frequency sensing algorithm is presented. Simulation results are given to present the effectiveness of the proposed technique for application to detection of carrier-frequency type signal and recognition of wideband signal in communication.

In this paper, we present a directional interpolation filter in which the minimum and maximum pixels in the given window are excluded. Image pixels within a predefined window are ranked and classified as minimum-maximum or exclusive level, and then passed through the interpolation and identity filters, respectively. Extensive simulations show that the proposed filter performs better than other nonlinear filters in preserving desired image features while reducing impulse noise effectively.

In this paper, we present a minimum-maximum exclusive weighted-mean filtering algorithm with adaptive window. Image pixels within the varying size of the window are ranked and classified as minimum-maximum and median levels, and then passed through the weighted-mean of median level and identity filters, respectively. The filtering window size is adaptively increasing according to noise ratio without noise measurement. Extensive simulations show that the proposed filter performs better than other median/rank-type filters in removing impulse noise of highly corrupted images.