In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. Current research also highlights the applications of biofield study in the future, i.e., and its further study will open up new gateways for investigation and bring remarkable changes in society. This research article highlights a method for the visualization, analysis, and medical benefits of biofield measurement. Due to its invisibility (through naked eyes) and weak strength, scientific measurements, analysis and belief of its existence is questionable. CAM practitioners challenge that this bioenergy has mind-like properties reflecting ones' physiological thought process, inner qualities, wellness, organ coordination, and others. It is a field of energy around every seen and unseen, living and non-living, holds a higher order of dynamic information within itself. The biofield is an extremely weak electromagnetic bio-information field, which is very hard to measure and visualize. Due to the lack of its scientific documentation and examination, usage and belief of biofield in various domains are not very popular. The previous study shows the substantial evidence of its existence and its impact on the health and thought process of an individual. The proposed method removes Gaussian noise and the edges are better preserved with less computational complexity and this aspect makes it easy to implement in hardware.Ĭomplementary and Alternative Medicine (CAM) researchers' committee stated a term called Biofield in 1994. Experimental results show the superior performance of the proposed filtering algorithm compared to the other standard algorithms in terms of both subjective and objective evaluations.

The performance of the algorithm is tested and compared with standard mean filter, wiener filter, alpha trimmed mean filter K-means filter, bilateral filter and recently proposed trilateral filter. The proposed method is an optimum solution for these requirements. Noise removing with edge preservation and computational complexity are two conflicting parameters. In the second stage, the center pixel is replaced by the mean value of the some of the surrounding pixels based on a threshold value. The algorithm initially estimates the amount of noise corruption from the noise corrupted image. In this paper, a new fast and efficient algorithm capable in removing Gaussian noise with less computational complexity is presented.

Krita gaussian noise reduction