In many imaging applications, producing a high resolution image is required. The term resolution refers to the number of pixels in an image. An image with higher resolution has more pixels and represents more accurate and detailed information. Resolution of an image depends on the imaging device. But due to the limitation of imaging devices and environmental conditions, captured images do not have sufficient resolution. One way to achieve this goal is to use powerful and costly cameras that leads to more cost and energy consumption. Thus, using a sensor with higher resolution is not a suitable solution. In the case of having a sensor with fixed resolution, we would like to find a solution to increase image resolution. Super resolution image reconstruction is a technique to reconstruct a high resolution image from a set of blurred and noisy low resolution images of a scene. These low resolution images are down-sampled, shifted and in some cases are rotated version of the original scene. Super resolution techniques use given information of different images (i.e. shift and rotation) for image recovery. Hence, super resolution is possible only if there is relative movement between pixels from one image to another. Super resolution has several applications in areas of video surveillance, remote sensing, satellite imaging, medical imaging, video standard conversion, etc. During last decades, several super resolution methods have been introduced to increase the resolution of an image. Optimization algorithms based on swarm-intelligence have been developed and applied to various engineering fields over recent years. Process of honey bee mating has been considered as an optimization method in which the search algorithm is iired by the process of mating in real honey-bee. In this thesis, a new method to enhance the resolution of an image based on Honey Bee Mating Optimization algorithm is presented. The experimental results show better performance of the proposed algorithm as compared with some other super-resolution methods. Nowadays, application of wireless camera sensor network has increased significantly. In surveillance networks which are the most important type of camera sensor networks, the goal is to capture images with high resolution from a desired scene. By applying the proposed super resolution algorithm in camera sensor networks, high resolution images from monitoring area could be generated. Generated high resolution images satisfy requirements of surveillance application of camera sensor networks. Energy consumption is the other important issue in wireless camera sensor networks. Therefore, selecting a subset of camera sensor nodes that decreases energy dissipation must be considered. By using suitable camera selection algorithm to maximize the coverage and minimize the energy dissipation, network performance is increased. Keywords: Super-Resolutio image reconstruction, Honey Bee Mating Optimization algorithm, Camera sensor network.