Research on wavelet-based contourlet transform algorithm for adaptive optics image denoising

Li, Dongming; Zhang, Lijuan; Yang, Jian; Su, Wei

doi:10.1016/j.ijleo.2016.02.042

Cited by 20 publications

(12 citation statements)

References 6 publications

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“…The readout noise satisfies an additive Gaussian model [17], and the degradation process can be represented as

i (x, y) = o (x, y) \otimes h (x, y) + c (x, y), (x, y) \in Ω,

where

(x, y)

is the spatial coordinate in the image,

o (x, y)

is the original image,

h (x, y)

refers to the PSF,

i (x, y)

represents the degraded image acquired by the image sensor,

c (x, y)

is the noise,

Ω

is the region of the object image, and ⊗ stands for the convolution operation.…”

Section: Frame Selection Methods and Psf Modelmentioning

confidence: 99%

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

Sun

Yang

et al. 2017

Sensors

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An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

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“…The readout noise satisfies an additive Gaussian model [17], and the degradation process can be represented as

i (x, y) = o (x, y) \otimes h (x, y) + c (x, y), (x, y) \in Ω,

where

(x, y)

is the spatial coordinate in the image,

o (x, y)

is the original image,

h (x, y)

refers to the PSF,

i (x, y)

represents the degraded image acquired by the image sensor,

c (x, y)

is the noise,

Ω

is the region of the object image, and ⊗ stands for the convolution operation.…”

Section: Frame Selection Methods and Psf Modelmentioning

confidence: 99%

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

Sun

Yang

et al. 2017

Sensors

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“…In [36], we proposed a wavelet-based contourlet transform (WBCT) method to image denoising. This method is implemented through combining with BayesShrink theory to estimate the threshold and then improving the adaptive method of selecting threshold, finally obtaining the optimal threshold.…”

Section: B Wbct-based Enhancement For High-pass Subbandsmentioning

confidence: 99%

“…In this paper, for the high-pass subbands of the retinal image after docomposition(based on the 2D DTCWT), we use the WBCT method for enhancement and obtain the enhanced subimages. The WBCT method is summarized in [36].…”

Section: B Wbct-based Enhancement For High-pass Subbandsmentioning

confidence: 99%

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Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method

Zhang

Sun

et al. 2019

IEEE Access

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Retinal image processing is very important in the field of clinical medicine. As the first step in retinal image processing, image enhancement is essential. Because the details of a retinal image are complex and difficult to enhance, we present a robust retinal image enhancement algorithm via a dual-tree complex wavelet transform (DTCWT) and morphology-based method in this paper. To begin with, we utilize the pre-processing method to the captured retinal images. Then, the DTCWT is applied to decompose the gray retinal image to obtain high-pass subbands and low-pass subbands. Then, a Contourlet-based enhancement method is applied to the high-pass subbands. For the low-pass subbands, we improve the morphology top-hat transform by adding dynamic multi-scale parameters to achieve an equivalent percentage enhancement and at the same time achieve multi-scale transforms in multiple directions. Finally, we develop the inverse DTCWT method to obtain the enhanced retinal image after processing the low-frequency subimages and high-frequency subimages. We compare this approach with enhancement based on the adaptive unsharp masking, histogram equalization, and multi-scale retinex. We present the test results of our algorithm on 440 retinal images from the DRIVE and the STARE databases. The experimental results show that the proposed approach can achieve better results, and might be helpful for vessel segmentation. INDEX TERMS Retinal image, dual-tree complex wavelet transform, top-hat transform, image enhancement.

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“…Therefore, for the high-frequency coefficients of the images, we adopt the fusion rule that adds the different coefficients with the weighting factors, which can be seen in Eq. (13). As a result, we make…”

Section: Sar Image De-noising Based On Residual Image Fusion and Sparmentioning

confidence: 99%

SAR Image De-noising Based on Residual Image Fusion and Sparse Representation

2019

KSII TIIS

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Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

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Research on wavelet-based contourlet transform algorithm for adaptive optics image denoising

Cited by 20 publications

References 6 publications

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method

SAR Image De-noising Based on Residual Image Fusion and Sparse Representation

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