Recently, image de-noising algorithm based on sparse representation has received an increasing amount of attention. Such algorithms proposed a comprehensive sparse representation model, by solving the sparse coding problem and choosing the proper method for dictionary updating to achieve better de-noising results. Therefore, the construction of learning dictionary has become one of the key problems that limit the de-noising effectiveness. The non-locally centralised sparse representation de-noising algorithm uses principal component analysis method to achieve dictionary updating. Nevertheless, the instability of a single complete dictionary in sparse coding leads to erratic result in the process of the original image restoration. In this study, the authors present a new method named generalised non-locally centralised sparse representation algorithm. In the proposed method, the authors cluster the training patches extracted from a set of example images into subspaces, and then train dictionaries for subspaces by sparse analysis k-singular value decomposition dictionary, which is utilised to construct coded sub-block dictionary to avoid the instable results caused by a single dictionary. Experiments show that the improved method has better signal-to-noise ratio and de-noising effect compared with other methods.
Some classical filters, such as bilateral filter, nonlocal means (NLM) filter, and locally adaptive regression kernel, have proven to have a good performance in image denoising. However, there is one shortcoming, i.e., they cannot control the denoising strength very well. As for synthetic aperture radar (SAR) images, due to the special multiplicative noise, the denoising process becomes more complicated. A diffusion iterative filter can enhance the performance of a kernel in the denoising process but will lose some latent details and important targets from the underlying SAR image. In contrast, an iterative boosting filter can preserve these latent details of the SAR image well, although the improvement of the kernel performance is not very desired. By adopting the advantages of diffusion and boosting of the two iterative methods, an adaptive iterative risk estimator minimum mean square error (Min-MSE) method is proposed, which is mainly based on the Min-MSE to adaptively get the optimal iterative method and the corresponding optimal iterative number. The analysis of the experimental results and the comparison with some other state of the art methods demonstrate that our proposed method can improve the performance of an NLM filter and effectively suppress the SAR image speckle. Ji et al.: Synthetic aperture radar image despeckling based on adaptive iterative risk estimator Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/26/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Then we can get the MSE by summing the values of Eqs. (19) and (20): Journal of Electronic Imaging 043001-3 Jul∕Aug 2015 • Vol. 24(4) Ji et al.: Synthetic aperture radar image despeckling based on adaptive iterative risk estimator Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/26/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxFig. 4 Comparison of denoising performance on SAR1: (a) SAR1 image; (b) PDE filtering result; (c) MAP filtering result; (d) NLM filtering result; (e) Lee filtering result; and (f) proposed method result. Ji et al.: Synthetic aperture radar image despeckling based on adaptive iterative risk estimator Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/26/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
There are some problems with traditional mark replacement and accurate detection of overprint deviation, this paper presents a new method which can detect the parameter of the no mark overprint. The method is based on the basis of mathematical morphology to determine the sign of the multi-structural elements and multi-scale morphological edge, by using detection algorithm to extract image edge; and then the image edge was fitted by using least square fitting algorithm, so the sub-pixel center coordinates of the overprint mark can be obtained; Finally, accurate data of the overprint deviation can be obtained by using deviation operation formula. Theoretical analysis and experimental results show that the overprint deviation detection has high precision, which can help to improve the accuracy, real-time and automatic control of overprint. So it can ensure that the whole picture's tone and hue of printing image can be reproduced accurately.
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