Biblio

Signals get sampled using Nyquist rate in conventional sampling method, but in compressive sensing the signals sampled below Nyquist rate by randomly taking the signal projections and reconstructing it out of very few estimations. But in case of recovering the image by utilizing compressive measurements with the help of multi-resolution grid where the image has certain region of interest (RoI) that is more important than the rest, it is not efficient. The conventional Cartesian sampling cannot give good result in motion image sensing recovery and is limited to stationary image sensing process. The proposed work gives improved results by using Radial sampling (a type of compression sensing). This paper discusses the approach of Radial sampling along with the application of Sparse Fourier Transform algorithms that helps in reducing acquisition cost and input/output overhead.

Aerial photography is fast becoming essential in scientific research that requires multi-agent system in several perspective and we proposed a secured system using one of the well-known public key cryptosystem namely NTRU that is somewhat homomorphic in nature. Here we processed images of aerial photography that were captured by multi-agents. The agents encrypt the images and upload those in the cloud server that is untrusted. Cloud computing is a buzzword in modern era and public cloud is being used by people everywhere for its shared, on-demand nature. Cloud Environment faces a lot of security and privacy issues that needs to be solved. This paper focuses on how to use cloud so effectively that there remains no possibility of data or computation breaches from the cloud server itself as it is prone to the attack of treachery in different ways. The cloud server computes on the encrypted data without knowing the contents of the images. After concatenation, encrypted result is delivered to the concerned authority where it is decrypted retaining its originality. We set up our experiment in Amazon EC2 cloud server where several instances were the agents and an instance acted as the server. We varied several parameters so that we could minimize encryption time. After experimentation we produced our desired result within feasible time sustaining the image quality. This work ensures data security in public cloud that was our main concern.

Image sharpness measurements are important parts of many image processing applications. To measure image sharpness multiple algorithms have been proposed and measured in the past but they have been developed with having out-of-focus photographs in mind and they do not work so well with images taken using a digital microscope. In this article we show the difference between images taken with digital cameras, images taken with a digital microscope and artificially blurred images. The conventional sharpness measures are executed on all these categories to measure the difference and a standard image set taken with a digital microscope is proposed and described to serve as a common baseline for further sharpness measures in the field.

A number of blind Image Quality Evaluation Metrics (IQEMs) for Unmanned Aerial Vehicle (UAV) photograph application are presented. Nowadays, the visible light camera is widely used for UAV photograph application because of its vivid imaging effect; however, the outdoor environment light will produce great negative influences on its imaging output unfortunately. In this paper, to conquer this problem above, we design and reuse a series of blind IQEMs to analyze the imaging quality of UAV application. The Human Visual System (HVS) based IQEMs, including the image brightness level, the image contrast level, the image noise level, the image edge blur level, the image texture intensity level, the image jitter level, and the image flicker level, are all considered in our application. Once these IQEMs are calculated, they can be utilized to provide a computational reference for the following image processing application, such as image understanding and recognition. Some preliminary experiments for image enhancement have proved the correctness and validity of our proposed technique.