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.

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

Application of improved wavelet adaptive threshold de-noising algorithm in FBG demodulation

Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Design of a lightweight robotic arm for kiwifruit pollination

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.

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

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

To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.

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Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

This paper proposed target image in a subpixel level matching algorithm for binocular CCD ranging, which is based on the principle of binocular CCD ranging In the paper, firstly, we introduced the ranging principle of the binocular ranging system and deduced a binocular parallax formula Secondly, we deduced the algorithm which was named improved cross-correlation matching algorithm and cubic surface fitting algorithm for target images matched, and it could achieve a subpixel level matching for binocular CCD ranging images Lastly, through experiment we have analyzed and verified the actual CCD ranging images, then analyzed the errors of the experimental results and corrected the formula of calculating system errors Experimental results showed that the actual measurement accuracy of a target within 3 km was higher than 052%, which meet the accuracy requirements of the high precision binocular ranging

/pdf/target-image-matching-algorithm-based-on-binocular-ccd-3v83z8k2zc.pdf

Target Image Matching Algorithm Based on Binocular CCD Ranging

To improve the adaptive optics (AO) image’s quality, we study the AO image restoration algorithm
based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the
wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF).
Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution
issue. The image restoration experiments are performed to verify the image restoration effect of our proposed
algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we
can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44%
respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is
significantly improved.

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

Aiming at the situation of current Chinese language resources shortage ,this paper proposes semantically selectional preferences of unsupervised learning method, and presents a strategy of obtaining verb-noun semantic collocation in Chinese. An approach of Chinese semantic preference learning, which is based on Latent Semantic Clustering model and Expectation Maximization Algorithm. First, the parameters are initialized randomly. Second, a certain number of training iterations is performed until convergence. Each iteration consists of expectation step and maximization step. Finally, the semantic association between verbs and nouns are calculated as a measure of its matching probability. This method can be used on Chinese without syntax-annotated corpora. Lots of experiment results show that LSC provides proper patterns of verb-noun collocation semantically. The algorithm converges quickly.

Dongming Li

Papers

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

Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

Target Image Matching Algorithm Based on Binocular CCD Ranging

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

Chinese Learning of Semantical Selectional Preferences Based on LSC Model and Expectation Maximization Algorithm