Video is fast becoming the most common medium for media content in the present era. It is especially helpful in security situations for the detection of criminal or threat-related activity. Police routinely use videos as evidence in the analysis of criminal cases. It is important in such applications to get a high-quality still image from such videos. However, there are situations where the images are blurred and have artifacts as they are extracted from moving video repositories. A practical solution to this problem is to sharpen these images using advanced processing techniques to obtain higher display quality. Due to vast amount of data, it is extremely important that any such enhancement technique satisfy real-time processing constraints, in order for it to be usable by the end user. In this paper, a blind image sharpness metric is proposed using a combination of edge and textural features. Edges can be detected using different methods like Canny, Sobel, Prewitt and Roberts that are commonly accepted in the image processing literature. The Canny edge detection method typically provides better results due to extra processing steps and can be effectively used as a model feature extractor for the image. Wavelet processing based on the db2, sym4, and haar is also utilized to extract texture features. The normalized luminance coefficients of natural images are known to obey the generalized Gaussian probability distribution. Consequently, this characteristic is utilized to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. The extracted features are then merged together to obtain the sharpened image. The principle behind image formation is to merge the wavelet decompositions of the two original images using fusion methods applied to the approximation and details coefficients. The two images must be of the same size and are supposed to be associated with indexed images on a common color map. It is worth noting that the image fusion results are more consistent with human subjective visual perception of image quality, ground truth data for which is obtained from publicly available databases. Popular standard images such as Cameraman and Lena are used for the experiments. Results also show that the proposed method provides better objective quality than competing methods.
Internet of Things (IoT), an emerging network of physical objects, acts as catalyst for the future connected world. It is estimated that there will be around 50 billion connected objects by year 2020. An IoT enabled connected world improves the way human live and interact with surroundings. Through IoT valuable information and services are available to humans on demand and in real time. But these information and services may also cause harm at certain level if not thoroughly observed. With the advent of IoT, the future of the connected world will face new types of security threats since more than half of the total connected objects today are exposed to such threats and vulnerability and this number may increase as more devices are getting connected to internet. Security is the major concern in designing IoT systems since the data collected by IoT objects may be critical and also data transmitted and processed by overall IoT system may be sensitive and may lead to issues with safety, privacy, authorization and authenticity etc. Therefore while taking advantage of IoT we must also consider the ways, to the highest possible degree, to prevent the future IoT connected world from harming us. Cyber security in IoT deals with protecting connected objects for data authorization, authentication, tempering and losses as well as identifying potential risks to the system. This paper provides a brief review on how to adopt security practices in designing IoT systems to make them secure and safe.