Electrospinning technology has been widely used in the past few decades to prepare nanofibrous scaffolds that mimic extracellular matrices. However, traditional two‐dimensional (2D) electrospun nanofibrous mats still have some inherent disadvantages for bone tissue engineering, such as limited cell infiltration and lack of three‐dimensional (3D) structure. The development of 3D electrospun scaffolds with larger pore sizes and porosity provides new perspectives for electrospinning‐based tissue engineering scaffolds. However, there are still some challenges and areas for improvement. In this review, the applications of 3D electrospun nanofibrous scaffolds in the field of bone tissue engineering from its fabrication methods to bio‐functionalization are summarized, with the aim of providing new insights into the design of electrospinning‐based bone tissue engineering scaffolds.
No-reference image quality assessment is of great importance to numerous image processing applications, and various methods have been widely studied with promising results. These methods exploit handcrafted features in the transformation or space domain that are discriminated for image degradations. However, abundant a priori knowledge is required to extract these handcrafted features. The convolutional neural network (CNN) is recently introduced into the no-reference image quality assessment, which integrates feature learning and regression into one optimization process. Therefore, the network structure generates an effective model for estimating image quality. However, the image quality score obtained by the CNN is based on the mean of all of the image patch scores without considering the human visual system, such as edges and contour of images. In this paper, we combine the CNN and the Prewitt magnitude of segmented images and obtain the image quality score using the mean of all the products of the image patch scores and weights based on the result of segmented images. Experimental results on various image distortion types demonstrate that the proposed algorithm achieves good performance.Keywords No-reference image quality assessment · Convolutional neural networks (CNNs) · Graph-based image segmentation · Prewitt magnitude
A novel iris biometric watermarking scheme is proposed focusing on iris recognition instead of the traditional watermark for increasing the security of the digital products. The preprocess of iris image is to be done firstly, which generates the iris biometric template from person's eye images. And then the templates are to be on discrete cosine transform; the value of the discrete cosine is encoded to BCH error control coding. The host image is divided into four areas equally correspondingly. The BCH codes are embedded in the singular values of each host image's coefficients which are obtained through discrete cosine transform (DCT). Numerical results reveal that proposed method can extract the watermark effectively and illustrate its security and robustness.
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