A comparison study of processing and visualization in image analytics

Image analytics is a process to evaluate and mine the insight about digital images. The emergence of QR-code-technology is a case, but practical cases include face as well as text recognition and analysis. This article explores smoothing approaches to enrich an image by filtering negative elements and stressing positive ones. Image smoothing is a common process and significant in mining digital images. The paper strengthens an appropriate algorithm for sharpening images by applying linear and non-linear algorithms. Sub-standardized and degraded images need to be previously sharpened prior to computer vision. It is found that the average smoothing algorithm technique outperforms other approaches regarding noise filtering as it focuses on static window size and computes the edges. Besides, it is also simply carried out in computation.