Human visualization system based intensive contrast improvement of the collected COVID-19 images

Abstract

Enhancement <span>and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of spee<a name="_GoBack"></a>ding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time-wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology.</span>

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

“…The Wiener filter is a popular image enhancement technique that is utilized in a variety of applications such as linear prediction, signal recovery, echo cancellation, channel equalization, and identification systems. The primary purpose of this approach is to recover the image while lowering the error value by reducing noise [27]. The Wiener filter is a statistically based filtering tool.…”