The purpose of this project is to investigate the issue of removing camera shake blur from a single image. The setup of the issue under investigation includes a camera taking a picture under conditions that favor a non-sharp result. These conditions may include dim light or camera motion during a shot. The result is a superposition of pixels of the sharp image leading to a blurry image with unclear details. This is because the blur has removed the high frequency components of the taken image. Dealing with such a matter is most important since there are several cases that blurry camera shots contain very important information for research, medical or image matters and the fact is that these shots cannot be retaken. Such cases can be astronomical images, car plate images or images from medical scans and microscopy. This is also a problem in everyday life photos. For example, pictures from friends’ reunion, wedding pictures or family pictures.

As input we use a single image having no other information regarding the scene the people or the objects present in the shot nor do we possess any other information regarding the equipment user or the user. Additionally, it is assumed that the method will be implemented for embedded systems processors and specifically for the ANDROID platform, meaning that a fast and lightweight implementation is in order. To cope with the aforementioned matters, we present from the current literature the most common methods and after a presenting a benchmarking along with the criteria we select the most suitable method.


Obstructive lung diseases (e.g. asthma & COPD) are life-long inflammatory diseases that result in narrowing of the lung airways, reducing the airflow that reaches the oxygen exchanging areas. The knowledge and understanding of the pathophysiology behind these diseases could lead to improved diagnosis and assessment, through the development of computational models that take into account details related to lung geometry, lung mechanical features and the dynamics of the fluid motion inside the lungs. Although there are several works that considers some of the aforementioned characteristics, none of them provides a fully integrated solution. To overcome this limitation, we have developed a software tool that combines geometry processing methods with 3D computational fluid dynamics (CFD), allowing us to simulate in a realistic way the effects of bronchoconstriction in the dynamics of the air flow in the airways of the lung. The developed tool is expected to provide useful information regarding the way that either drug or other harmful particles are being dispersed inside the lungs during different levels of inflammation.