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Digital imaging has significantly influenced the outcome of research in various disciplines. For example, artificial intelligence and robotics, biometric security, multimedia and image processing, etc. Technically, image processing and the Human Visual System (HVS) relies heavily on image enhancement to improve the content of the image. One of the biggest challenges in image processing is detail enhancement due to halo artefacts and gradient inversion artefacts at edges. It has been used to enhance the visual quality of an image. Most algorithms that used to enhance the detail of an image essentially depend on edge-preserving decomposition techniques. in general, the image consist of two major elements are a base layer and a detail layer, which extracted by edge-preserving decomposition algorithms. The detail layer is enhanced to improve the details of the generated image. we propose in this paper, a new model to preserve the sharp edges and achieve better visual quality than the existing norm-based algorithm to enhance the details of the image. Experiments show that the proposed method reduces the distortion at the edges. It improves the details of the generated image significantly.

Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.

With the advent of technology, electronic learning (e-learning) has become a key aspect of distance learning, making it easier, faster, and more global. With e-learning, teachers have found it easy to deliver education, while students have found new opportunities to learn. There have been several advances to e-learning as a resource for distance learning, with its major features being the use of electronic mediums to facilitate learning. This study is the first of its kind in Iraq that aims to explore the instructional needs of teachers with regards to e-learning for computer science at the secondary school level in Iraq. The study aims to identify the perceptions of secondary school teachers of computer science towards the use of e- learning methods and to also investigate the training needs of teachers for the use of e-learning for teaching of computer science in Iraq. Results indicated that e-learning resources available in the schools surveyed are not enough and the teachers are not making adequate use of e-learning resources. Also, while teachers were aware of the efficiency of e-learning for pedagogy, the schools do not have adequate support mechanisms in place. Teachers were also aware of the competitive edge that e-learning offers them, but did not have adequate training to enable them to use it effectively. Therefore, authorities have to establish a training plan to get teachers comfortable with technology. Such training should start with the basics of e-learning, so that teachers with no knowledge of technology can be carried along.