Hadeer Adel

In this paper, a novel single image deblurring technique based on sparse representation and radon transform is presented. The Sparse representation is used to make an initial estimation of the latent sharp image. Then, a set of directional filters are applied to the blurry and noisy image to reduce the noise while maintaining the blurry information on the orthogonal direction. After that an initial kernel estimation at different angles is performed using the initial latent image that was produced using sparse representation. Each estimated kernel is transformed to radon transform and the set of projections at different angles are stored. An inverse radon transform is applied to make a final kernel estimation. Finally, the Wiener deconvolution is performed to estimate the final latent sharp image. Experimental results showed the effectiveness of the proposed technique. The best obtained PSNR is 28.233 at 0.04 noise ratio, whereas the best obtained SSIM is 0.896769 at also 0.04 noise ...

Different systems focus on the analysis of the motion of the human body, meanwhile these systems face a lot of challenges such as the small range and the high cost. This paper represents a proposed system for detecting the motion of a moving body called Large Area Motion Tracking (LAMT). This system enables the researchers to study and follow up the human body motion in large areas. This can be achieved by developing a video display of a laser beam motion using a photo detector and an optical system. The main advantages of the proposed system are the speed, the low price, the accurate run and the real-time detection capability., also it will be suitable for the indoor and the outdoor applications.

Image blur is a common problem that occurs when recording digital images due to camera shake, long exposure time, or movement of objects. As a result, the recorded image is degraded and the recorded scene becomes unreadable. Recently, the field of blur removal has gained increasing interest in a lot of researches. The problem is known as blind deconvolution if the only available information is the blurred image and there is no knowledge about the blurring model or the Point Spread Function (PSF). In this case, the basic target of the process is to recover both the blur kernel and the deblurred (latent) image, simultaneously. In this paper, we introduced a comprehensive study on the image deblurring, type of blur, noise model and finally a comparative study of different image deblurring techniques. We performed several experiments to evaluate these techniques in terms of performance, blur type, Peak Signal to Noise Ratio and structural similarity (SSIM).

This paper presents an alternative event detection model based on the integration between the DistilBERT and a new meta-heuristic technique named the Hunger Games Search (HGS). The DistilBERT aims to extract features from the text dataset, while a binary version of HGS is developed as a feature selection (FS) approach, which aims to remove the irrelevant features from those extracted. To assess the developed model, a set of experiments are conducted using a set of real-world datasets. In addition, we compared the binary HGS with a set of well-known FS algorithms, as well as the state-of-the-art event detection models. The comparison results show that the proposed model is superior to other methods in terms of performance measures.