Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Shukla, Vedant; Khandekar, Prasad; Khaparde, Arti

doi:10.1016/j.bspc.2021.103225

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…The rice reflectance images and the sub-band images decomposed by level 1 Haar wavelet transform are shown for reference in Figure 3 . Most of the energy of the image was concentrated in the LL sub-band and was very high ( Shukla et al, 2022 ). The LL sub-band reflected the outline of the image and contained most of the information of the image.…”

Section: Methodsmentioning

confidence: 99%

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

et al. 2022

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Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

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Section: Methodsmentioning

confidence: 99%

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

et al. 2022

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“…In transformation, domain noise is estimated using DWT coefficients [12]. Both spatial and transformation domain are used to find the noise amount [13].…”

Section: Literature Surveymentioning

confidence: 99%

Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

Kavitha

Prakash²,

Alhomrani

et al. 2023

Contrast Media & Molecular Imaging

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The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.

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“…Weight coefficients (elements of matrices W 1 and W 2 and vectors B 1 and B 2 ) were determined during calculation in the ANN learning cycle. The values of these coefficients were revised by applying optimization procedures to minimize the error between the network and experimental outputs [44,46,47], according to the sum of squares (SOS). The well-known BFGS algorithm was used to accelerate and consolidate the convergence in the finding the solution [48].…”

Section: Ann Modelingmentioning

confidence: 99%

Model for Determining Noise Level Depending on Traffic Volume at Intersections

et al. 2022

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The negative external effects caused by traffic growth have been recognized as the main factors that degrade city quality of life. Therefore, research around the world is being conducted to understand the impact of traffic better and find adequate measures to reduce the negative impact of traffic growth. The central part of this research consists of mathematical models for simulating the negative consequences of congestion and noise pollution. Four non-linear models for determining noise levels as a function of traffic flow parameters (intensity and structure) in the urban environment were developed. The non-linear models, including two artificial neural networks and two random forest models, were developed according to the experimental measurements in Novi Sad, Serbia, in 2019. These non-linear models showed high anticipation accuracy of the equivalent continuous sound level (Laeq), with R2 values of 0.697, 0.703, 0.959 and 0.882, respectively. According to the developed ANN models, global sensitivity analysis was performed, according to which the number of buses at crossings was the most positively signed influential parameter in Laeq evaluation, while the lowest Laeq value was reached during nighttime. The locations occupied by frequent traffic such as Futoska and Temerinska positively influenced the Laeq value.

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Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Cited by 13 publications

References 44 publications

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

Model for Determining Noise Level Depending on Traffic Volume at Intersections

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