Using digital images at present are increased rapidly in many fields such as in solving big problems, with the extraordinary appearance of them in all areas of life, in medicine, agriculture, industry, the Internet, and others, where their use is extensive and is considered a source of information for technological progress. One of the important use is stitching the images, also called mosaic images. The stitching images means a grouping of images for the same sense with the overlapping areas to be a panoramic image of high resolution and wide width. With the modification and development of the algorithms used in this field in recent years, it has become one of the essential branches of image processing. There are many applications of stitching, used in maps and satellites, knowledge and positioning, etc. So this summary article will provide a set of image stitching techniques and investigate its use in terms of advantages, disadvantages, and accuracy for each one of them with comparative studies of several research papers in this field for the period of years (2017 – 2020). Therefore, this article may be useful for researchers working in this field to benefit and develop stitching algorithms in terms of discovering features and matching them to create a useful, problem-free, and high-resolution panoramic image.
Nowadays, huge digital images are used and transferred via the Internet. It has been the primary source of information in several domains in recent years. Blur image is one of the most common difficult challenges in image processing, which is caused via object movement or a camera shake. De-blurring is the main process to restore the sharp original image, so many techniques have been proposed, and a large number of research papers have been published to remove blurring from the image. This paper presented a review for the recent papers related to de-blurring published in the recent years (2017-2020). This paper focused on discussing various strategies related to enhancing the software's for image de-blur. The aim of this research is to help researchers to understand the current algorithms and techniques in this field, and eventually may developing new and more efficient algorithms for enhancing blurred images.
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