Security Inspection Image Processing Methods Applying Wavelet Transform Filters on Terahertz Active Images

Abstract

Clarity of Terahertz image is essential at various security checkpoints to avoid life’s dangers and treats. However, Terahertz images are distorted by noise. Noise is frequently present in digital images during the image collection, coding, delivery, and processing phases. It is extremely difficult to remove noise from digital images without prior knowledge of the noise model. Wavelet transforms have gained popularity as a tool for image denoising. In this paper, we advance a solution to this challenge using Global Threshold selection as well as wavelet transform filters. When compared to denoising Gaussian noise at the same percentage induced, biorthogonal is the most effective denoising filter for salt and pepper noise. As the salt and pepper noise increases from 20% to 60%, the hidden security image as our target varnishes or is overpowered by the induced salt and pepper noise. We discover that despite the fact that the bior 4.4 and sym4.0 wavelet transform filters prove powerful in denoising the image, it is still not clearer and that when an image is tainted by Gaussian noise, wavelet shrinkage denoising is nearly perfect in both bior4.4 and sym 4.0, whereas when the image is tainted by salt & pepper noise, wavelet shrinkage denoising is nearly perfect in both bior4.4 and sym 4.0.

Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks

“…Both the HL and LH sub-bands contain almost similar information and for the sake of efficiency, only the HL is investigated. To this end, we used the following mother functions: Haar, Daubechies2 (Db2), Symlet2 (Sym2), and Coiflet2 (Coif2) 21,22 . During the wavelet transforms, the input image is convolved with low-pass and high-pass filters and down sampled to obtain the wavelet sub-bands.…”