Many of the existing imaging systems produce images with blurry appearance due to various existing limitations. Thus, a proper sharpening technique is usually used to increase the acutance of the obtained images. The unsharp mask filter is a well-known sharpening technique that is used to recover acceptable quality results from their blurry counterparts. However, this filter often introduces an overshoot effect, which is an undesirable effect that makes the recovered edges appear with visible white shades around them. In this article, an amended unsharp mask filter is developed to sharpen different digital images without introducing the overshoot effect. In the developed filter, the image is smoothed by using the traditional bilateral filter and then blurred using a modified Butterworth filter instead of blurring it with a Gaussian low-pass filter only as in the traditional unsharp mask filter. Using this approach allowed to eliminate the overshoot effect and to recover better quality results. The proposed filter is assessed by using two modern image quality assessment metrics, real and synthetic-blurred images, and is compared with three renowned image sharpening techniques. Various experiments and comparisons showed that the proposed filter produced promising results with both real and synthetic-blurred images.

As known, the contrast is a highly important feature by which the visual quality of digital images can be judged as adequate or poor. Hence, many methods exist for contrast enhancement, where the complexity of those methods usually varies due to the utilization of different concepts. In this article, a simple yet efficient algorithm is introduced for contrast enhancement of digital images. The proposed algorithm consists of four distinct stages: In the first stage, the hyperbolic sine function is applied to provide a simple contrast modification. In the second stage, a modified power-law function is utilized to control the amount of contrast adjustment. In the third stage, the standard sigmoid function is used to remap the image pixels into an “S” shape, which can provide further contrast enhancement. In the final stage, a contrast stretching function is applied to remap the image pixels into their natural dynamic range. The performed computer experiments on different low-contrast images demonstrated the efficiency of the proposed algorithm in processing synthetic and real degraded images, as it provided better and clearer results when compared to several existing contrast enhancement algorithms. To end with, the proposed algorithm can be used as a contrast processing step in many image-related applications.

Higher education is encountering major alteration in many countries. As well, many innovative learning techniques are emerging due to the speedy growths of technology, internet and communication tools. This thrive of technology has changed various features and conceptions of the traditional learning environments. Recently, the attention has focused on electronic learning (e-learning), which is a form of education that has begun to find its way into different developing countries due to its high potential in improving the educational process. However, different real-world challenges emerged, in which they hampered the application of e-learning in developing countries. Thus, this study aims to identify the status of the actual level of e-learning application among private universities in the Kurdistan region of Iraq. It is believed that the results of this study will greatly ameliorate the currently utilized educational systems.

An inclement dusty weather can significantly reduce the visual quality of captured images and this consequently leads to hamper the observation of meaningful image details. Capturing images in such weather often leads to undesirable artifacts such as poor contrast, deficient colors or color cast. Hence, various methods have been proposed to process such unwanted event and recover vivid results with acceptable colors. These methods vary from simple to complex due to the variation of the used processing concepts. In this article, an innovative technique that utilizes tuned fuzzy intensification operators is introduced to expeditiously process poor quality images captured in an inclement dusty weather. Intensive experiments were carried out to check the processing ability of the proposed technique, wherein the obtained results exhibited its competence in filtering various degraded images. Specifically, it performed well in providing acceptable colors and unveiling fine details for the processed images.

Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process.

Observed images with bare eyes are always different than the acquired ones using an imaging system since the captured images are considered as the degraded versions of the original scene. These degradations may vary between image noise, lighting defects and blur. Therefore, this article addresses the field of computer forensics with image deblurring as the latent details that are indeed present in the captured images are concealed due to the blurring artifact. Moreover, the constant types of blur that are being dealt with in forensics are the motion and the out-of-focus blur. The motion blur occurs due to the motion of the recorded objects or the camera during the capturing process. The out-of-focus blur occurs due to lens defocusing errors. Different examples are provided to focus on the importance of deblurring forensic images. In addition, concise commentaries on deblurring methods, applications and blur types are deliberated for additional knowledge.