Moving shadow detection based on stationary wavelet transform and Zernike moments

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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“…Medical UIT can be divided into four types according to the principle of ultrasound imaging and different scanning methods [10].…”

Section: Principle Of Medical Ultrasoundmentioning

confidence: 99%

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Shen

Han

2021

Scientific Programming

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“…Some researchers used transform domain techniques to detect shadow region. Nagarathinam et al [16] used spatial wavelet transform (SWT) and Zernike moment (ZM) based method to detect moving shadow. The main drawback of this method is, SWT cannot capture edges properly and provides limited information along vertical, horizontal and diagonal direction.…”

Section: Related Workmentioning

confidence: 99%

Shadow detection from images using fuzzy logic and PCPerturNet

Chaki

2021

IET Image Processing

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Shadow detection is a challenging and essential task for interpreting the scene. Regardless of encouraging findings from current Deep Learning (DL) approaches used for shadow detection, the methods are also dealing with inconsistent situations where the visual representation of non-shadow and shadow regions is equivalent. In this article, a DL based approach is introduced for image pixel-level shadow detection. The proposed CNN-based approach, pattern conserver convolutional neural network (PCPerturNet) profits from a new design where shadow features are defined utilizing an effective skip-connection mapping arrangement. To make PCPerturNet robust from the change in brightness and contrast, several perturbed instances are generated by using a fuzzy-logic based method to train the system. Also, five types of augmentations are applied to images during training to make the system robust from the change in scale, orientation and flip. PCPerturNet derives and conserves shadow patterns in manifold layers and uses those layers progressively in several units to produce the shadow mask. The output of the proposed method is tested on two freely accessible databases and one self-created database where the accuracy rate obtained is 96.4%, 96.8%, and 89.4% which indicates that the proposed method outperforms the other shadow detection approaches used in the literature.

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“…The first work using this approach for images analysis has been presented by Teague M. [11]. Afterwards, many other authors have become interested by this approach [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Rearranged Descriptor Approach based on Radon Transform to Digits Recognition

Bechar¹,

Bessaid²,

Messadi³

2021

EEA

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In this paper, the Radon transform method is used to generate a set of rotation invariant characteristics. Experiments of our approach were carried out on a database of ten decimal digits (0 to 9) in 24 different orientations from 0° to 360 ° by a step of 15 °. A multilayer perceptron neural network is used in the classification phase to test the effectiveness of our approach. The proposed approach is noise-effective and leads to a classification rate equal to 100 % for images without noise and a classification rate equal to 95.2 for images with noise.

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Moving shadow detection based on stationary wavelet transform and Zernike moments

Cited by 13 publications

References 30 publications

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Shadow detection from images using fuzzy logic and PCPerturNet

Rearranged Descriptor Approach based on Radon Transform to Digits Recognition

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