Contribution study of monogenic wavelets transform to reduce speckle noise in digital speckle pattern interferometry

Zada, Sara; Tounsi, Yassine; Kumar, Manoj; Mendoza-Santoyo, Fernando; Nassim, Abdelkrim

doi:10.1117/1.oe.58.3.034109

Cited by 21 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, two-level DWT based on symmetry characteristics was employed to decompose the resulting image into seven high-frequency sub-band images and one low-frequency sub-band image. A number of researchers have utilized threshold methods based on hard [36], soft [10], and Bayesian [36,38] techniques [25,39,44,53]. In these threshold methods, a wavelet threshold value reflects the total information of the wavelet coefficients of each high-frequency sub-band image.…”

Section: Discussionmentioning

confidence: 99%

“…In a few experiments, algorithms employing a transform domain have been developed to eliminate speckle noise from images. Zada et al [25] proposed a speckle noise reduction algorithm based on the monogenic wavelet transform in digital images. This algorithm showed an outstanding speckle noise elimination ability but exhibited artifacts in the non-edge areas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Choi

2020

Symmetry

View full text Add to dashboard Cite

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Choi

2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…Speckle noise can result in granular appearance, limit the contrast, and reduce the signal-to-noise ratio (SNR) of OCT images, which can pose great difficulties to identify the detailed features of OCT images 22 , 23 . In the pre-processing step, wavelet transform can reduce the speckle noises of OCT images 24 .…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method

Wang

Zhong

Yao

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.

show abstract

“…The purpose of speckle suppression methods based on the convolutional neural network is to learn the nonlinear mapping relationships between clean images and corresponding noisy images. Since the 1980s, different methods for despeckling have been proposed based on various technologies, such as multilook processing [15][16][17][18], spatial domain filters [19][20][21][22], wavelet transform [23][24][25][26], nonlocal filtering [27][28][29][30], and total variation [31][32][33][34]. The multilook processing can suppress speckle noise simply and effectively, but this leads to reduction of resolution for SAR image.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

et al. 2021

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

show abstract

Contribution study of monogenic wavelets transform to reduce speckle noise in digital speckle pattern interferometry

Cited by 21 publications

References 33 publications

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

Contact Info

Product

Resources

About