Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Image denoising based on a CNN model

Abstract: In digital image processing, filtering noise to reconstruct a high quality image is an important work for further image processing such as object segmentation, detection, recognition and tracking, etc. In this paper, we will use a CNN model in deep learning for image denoising. Compared with traditional image denoising methods such as average filtering, Wiener filtering and median filtering, the advantage of using this CNN model is that the parameters of this model can be optimized through network training; whereas in traditional image denoising, the parameters of these algorithms are fixed and cannot be adjusted during the filtering, namely, lack of adaptivity. In this paper, we design and implement the denoising method based on a linear CNN model. Our experimental results show that the proposed CNN model can effectively remove Gaussian noise and improve the performance of traditional image filtering methods significantly.

A new kind of adaptive weighted median filter algorithm

Abstract: Aimed at the excellence and shortcoming of the standard median filtering algorithm and the adaptive median filtering algorithm as well as center weighted median filtering algorithm, a new adaptive weighted median filtering algorithm is proposed in this paper. The new algorithm first takes a decision whether the pixel under test is noise or not by comparing the block uniformity of the 3×3 window with one of the entire image, then adjusts the size of filtering window adaptively according to the number of noise points in the window. Finally the noise is removed by means of ameliorated median filtering algorithm. Experimental results clearly indicate that the proposed method has a better filtering effect than the existing methods such as standard median filter, adaptive median filter in terms of visual quality and quantitative measures.

Statistical Based Impulsive Noise Removal in Digital Radiography

Abstract: A new filter to restore radiographic images corrupted by impulsive noise is proposed. It is based on a switching scheme where all the pulses are first detected and then corrected through a median filter. The pulse detector is based on the hypothesis that the major contribution to image noise is given by the photon counting process, with some pixels corrupted by impulsive noise. Such statistics is described by an adequate mixture model. The filter is also able to reliably estimate the sensor gain. Its operation has been verified on both synthetic and real images; the experimental results demonstrate the superiority of the proposed approach in comparison with more traditional methods.

A Median Regularized Level Set for Hierarchical Segmentation of SAR Images

Abstract: An efficient strategy of image processing algorithms to deal with the speckle noise is to incorporate data knowledge and models into them In this letter, we introduce a hierarchical level set algorithm, which is fast and precise for multiregion segmentation of synthetic aperture radar (SAR) images Our algorithm performs curve regularization with a nonparametric median filter instead of using the curvature formulation, and hence it reduces the computation time The proposed algorithm also replaces the front propagation derivatives by morphological operations, and finally, the arithmetic-geometric distance measures the contrast between regions and controls the hierarchical segmentation We conducted experiments on synthetic and real SAR images modeled by the $\mathcal {G}_{I}^{0}$ distribution The performance evaluation of the proposed algorithm and two related methods comprises the computation time and measures based on segmentation accuracy and stochastic distance Overall, our segmentation algorithm performed faster and more precise on both synthetic and real SAR images