Face sketch synthesis refers to transform facial photos into sketches. Recent research on face sketch synthesis has achieved great success due to the development of Generative Adversarial Networks (GAN). However, these generative methods prone to neglect detailed information and thus lose some individual specific features, such as glasses and headdresses. In this paper, we propose a novel method called Feature Learning Generative Adversarial Network (FL-GAN) to synthesize detail-preserving high-quality sketches. Precisely, the proposed FL-GAN consists of one Feature Learning (FL) module and one Adversarial Learning (AL) module. The FL module aims to learn the detailed information of the image in a latent space, and guide the AL module to synthesize detail-preserving sketch. The AL Module aims to learn the structure and texture of sketch and improve the quality of synthetic sketch by adversarial learning strategy. Quantitative and qualitative comparisons with seven state-of-the-art methods such as the LLE, the MRF, the MWF, the RSLCR, the RL, the FCN and the GAN on four facial sketch datasets demonstrate the superiority of this method.

Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.