A Nonlinear Gradient Domain‐Guided Filter Optimized by Fractional‐Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing

Fang, Qionglin; Han, Xizhen

doi:10.1155/2021/8864906

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Initially, the global sparse disintegration has been utilized in order to preeliminate a portion of the surface to improve the performance of smoothing. Similarly, Fang and Han [ 5 ] proposed a nonlinear incline field-guided image enhancement technique where they proved optimal value as a model factor. Moreover, the fractional order calculus is utilized to gradient ancestry along with the impetus technique in order to train the neural network.…”

Section: Introductionmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

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Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.

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Section: Introductionmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

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“…where Γ is the Gamma function and h is the incremental step size. Expressing (21) in discrete-time representation with sampling period T…”

Section: Fractional Order Swarm Optimizationmentioning

confidence: 99%

“…Fractional calculus operators are also exploited to design novel recursive/adaptive algorithms as well as evolutionary/swarm computation heuristics for different optimization tasks involved in engineering and science applications. For example, fractional gradient descent/fractional least mean square algorithm was proposed for various applications including recommender systems [10], channel estimation [11], automatic identification system [12], power system optimization [13], economics [14], radar signal processing [15], system identification [16,17], Hammerstein output error identification [18], wireless sensor network [19], neural network optimization [20][21][22][23][24], chaotic time-series prediction [25,26], oscillator [27], vibration rejection [28], nonlinear AR-MAX identification [29] and parameter estimation of input nonlinear control autoregressive (IN-CAR) systems [29,30].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

et al. 2022

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In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of fractional differential/integral operators; fractional order swarming heuristics are also introduced and applied for better performance in different optimization tasks. This study investigates the nonlinear system identification problem of the input nonlinear control autoregressive (IN-CAR) model through the novel implementation of fractional order particle swarm optimization (FO-PSO) heuristics; further, the key term separation technique (KTST) is introduced in the FO-PSO to solve the over-parameterization issue involved in the parameter estimation of the IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates the parameters of an unknown IN-CAR system with robust performance in cases of different noise scenarios. The performance of the KTST-FOPSO is investigated exhaustively for different fractional orders as well as in comparison with the standard counterpart. The results of statistical indices through Monte Carlo simulations endorse the reliability and stability of the KTST-FOPSO for IN-CAR identification.

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“…( 2) ensures the consistency of the output image to the original image structure, and the regular term plays a constraint role, such as reducing artifacts. Some variants of GIF [10][11][12][13][14][15][16][17][18][19][20] have been proposed to improve the performance of GIF. For example, the weighted aggregate guided image filtering (WAGIF) [13] adopted exponential weighting on the data fidelity term to preserve the edge and remove noise, but still cannot avoid color diffusion and halo artifacts; The weighted guided image filtering (WGIF) [10] and gradient domain guided image filtering (GDGIF) [11] use local variance and gradient to adjust the regularization parameter  to reduce halo artifacts and noise.…”

Section: Related Workmentioning

confidence: 99%

“…But these methods have limited suppression of halo artifacts at discontinuous details and are prone to color diffusion. The work in [10,11,[18][19][20] aimed to suppress halo artifacts at sharp details by adjusting the regularization parameter with local variance [10,18,19] or gradient [11,20] of the image. These methods favor the first-order derivatives of the images to achieve good capabilities of edge preserving and artifact suppression, but they are not suitable to preserve the complex geometrical structures of the images such as corners and blob-like structures [21].…”

Section: Introductionmentioning

confidence: 99%

Hessian-based weighted guided image filtering

Xie

Cao

2022

Preprint

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Guided image filtering (GIF) is a popular edge-preserving smoothing technique, and the regularization parameter selection plays an important role in the performance of GIF. In this paper, we propose a new guided image filter based on Hessian matrix which consists of the second-order derivatives of an image. More specifically, a new structural measurement index is introduced by using the eigenvalues of the Hessian matrix first, which can distinguish the texture and flat regions of the image. Then the regularization parameter is adjusted based on this Hessian-based second-order structure measurement index, that is, a large regularization parameter is selected to improve the smoothness of the flat regions, while a small regularization parameter is set for the texture regions to preserve the image structure such as edges and corners. To further improve the quality of the filtered images, we also introduce a weighted averaging technique to the linear filter coefficients based on local variance. Experimental results show that the proposed Hessian-based weighted guided image filtering (HWGIF) method outperforms the state-of-the-art approaches in image processing applications such as edge-preserving denoising, detail enhancement, dehazing and HDR compression.

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A Nonlinear Gradient Domain‐Guided Filter Optimized by Fractional‐Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing

Cited by 5 publications

References 17 publications

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

Hessian-based weighted guided image filtering

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