This paper presents a measurement-driven framework to deal with the compressed sensing (CS) system design problem. Under this novel framework, the sparse coefficient matrix is calculated according to the low-dimension measurements, rather than updated along with the dictionary as in the traditional cases. Moreover, a new cost function is proposed to simultaneously optimize the sensing matrix and dictionary. In order to minimize this cost function, an iterative algorithm is carried out. In every iteration, the solutions of the sensing matrix and dictionary are derived analytically. Experiments are executed with real images, especially medical images. The results demonstrate the superiority of the designed CS system composed of the optimized sensing matrix and dictionary with improved performance for image compression and reconstruction.

In this paper, we show that there are a number of uncertainty principles for the local polynomial Fourier transform and local polynomial periodogram. Systematic analysis of uncertainty principles is given, explicit expressions of the uncertainty relations are derived, and an example using the chirp signal and the Gaussian window function is given to verify the expressions.

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Although promising results have been achieved in the area of traffic sign detection, little attention has been paid to text detection on traffic signs. In fact, in today's popular driver-less automobile industry, traffic sign text which brings abundant and valuable traffic information plays an important and indispensable role. In this work, we design an effective detector for traffic sign text, whose pipeline only consists of a preprocessing module to tackle with some complex situations, a Fully Convolutional Network (FCN) in which a Scale-transfer layer is proposed to speed up the network and a simple post-processing step. Extensive experiments on the Chinese traffic sign text dataset (CTST-1600), ICDAR 2013 and MSRA-TD500 show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications. We collect the Chinese text-based traffic sign dataset named CTST-1600, and it can be found at https://github.com/pummi823/test/blob/master/ CTST-1600. INDEX TERMS Scene text detection, multi-oriented text, convolutional neural network, residual network.