Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain

Saravani, Shahram; Shad, Rouzbeh; Ghaemi, Marjan

doi:10.1007/s11042-018-6153-8

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Iris biometric systems include all Iris recognition is used in jails for the recognition of prisoners. They all utilize iris recognition and immigration control at their borders (Saravani et al, 2018). For locating iris areas combined with reducing noise from eyelids, presented an intero-differential operator.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

KHAN,

BAIG

et al. 2023

qjssh

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An individual's identity has grown more essential for individuals to satisfy modern corporate the community's higher safety standards. Iris represents one of the finest and most precise biometric systems now in utilization, with multiple edge detection methods used. As a consequence, understanding the different kinds of edge detection algorithmic methods which are now under operation is crucial. Three edge detection methods are used to be assessed on iris data during the present research. These methodologies are carried out using the MATLAB environment, which provides the evaluation with verified results. CASIA and MMU are the datasets that are used for this purpose. In contrast, the results reveal that the canny edge detection method works quite efficiently. Visual quality is crucial in vision-based recognition of objects. The present research assesses the visual aesthetic using various quality indicators such as PSNR and MSE. When compared to PSNR and MSE, however, MSE is recognized as the best image quality statistic.

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Section: Literature Reviewmentioning

confidence: 99%

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

KHAN,

BAIG

et al. 2023

qjssh

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“…After WD, the HH subimage at the same scale contains more details of the original image, while the HL and LH subimages have less detailed features and larger approximate features [25]. erefore, when…”

Section: Wavelet Enhancement Model Designmentioning

confidence: 99%

Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

Fan

Huo

2021

Mathematical Problems in Engineering

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A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

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“…Part of the wavelet coefficients in d 1 k;n,m , d 2 k;n,m and d 3 k;n,m that correspond to the background details of noise and arm are smaller, while the wavelet coefficients related to the peak of gesture are larger. Therefore, the wavelet coefficients are shrinked by a threshold [35], and the larger coefficients are retained to reduce the influence of noise in the RDM image. Finally, the RDM is reconstructed using the processed wavelet coefficients.…”

Section: Image Enhancementmentioning

confidence: 99%

TS-I3D Based Hand Gesture Recognition Method With Radar Sensor

Wang

Zhou

et al. 2019

IEEE Access

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Aiming at the problems of the noise impact on the parametric image of hand gestures, the difficulty of gesture feature extraction, and the inefficient utilization of continuous gesture time sequential information, we propose a time sequential inflated 3 dimensions (TS-I3D) convolutional neural network approach for hand gesture recognition based on frequency modulated continuous wave (FMCW) radar sensor. Specifically, the FMCW radar is used to acquire the hand gesture data, and the range and speed of the gesture in each frame signal are calculated by 2 dimensions fast Fourier transform. Then, the range-Doppler map (RDM) is generated based on the relationship between motion parameters and frequency. The interference in RDM caused by people and the external environment is filtered out and the peak of hand gesture in RDM is further enhanced by wavelet transform. Finally, TS-I3D network is designed to extract the range and speed change information of the continuous gestures. The experimental results show that the average recognition accuracy rate of the hand gestures of the proposed method is 96.17%. INDEX TERMS FMCW radar, hand gesture recognition, interference filtering, deep learning, LSTM. I. INTRODUCTION With the development of advanced signal processing approaches and deep learning [1], [2], human-computer interaction has attracted much attention. Hand gesture recognition (HGR), as one of the most important way of human-computer interaction, gets rid of the limitations of people in traditional control devices, and has been widely used in various fields, i.e., smart home like switching TV channels, turning on the air conditioner or intelligent driving like answering a phone call. The existing HGR methods mainly contain three stages, namely hand gesture data acquisition, feature extraction, and gesture classification. Hand gesture data is mainly collected using optics camera [3]-[5] or radar [6]-[9]. Optical-based gesture recognition mainly applies cameras to capture gesture images, and then applies machine-learning methods [10], [11] for feature extraction and recognition. Coelho et al. [4] use Kinect to capture RGB and depth images of hand gestures, The associate editor coordinating the review of this manuscript and approving it for publication was Guan Gui.

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Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain

Cited by 12 publications

References 26 publications

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

TS-I3D Based Hand Gesture Recognition Method With Radar Sensor

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