This paper proposes a novel graph based learning approach to classify agricultural datasets, in which both labeled and unlabelled data are applied to the classification procedure. In order to capture the complex distribution of data, we propose a similarity refinement approach to improve the robustness of traditional label propagation. Then the refined affinity matrix is applied to label propag...

To remove impulsive noise, several variants of median-type filters are presented. They use some prior information provided by the probabilistic composition model of the class of the image being processed. This information is doubly applied; first, in elaborating a noise-estimation mask for the preselection of the pixels which must be filtered; and second, in the filtering process itself. The ai...

We present a practice-oriented, i.e. fast and robust, estimator for strong signal-dependent noise in medical low-dose X-ray images. Structure estimation by median filtering has shown to be superior to linear binomial filtering. Falsifications due to remaining structure in the estimated noise image are significantly reduced by iterative outlier removal.

Images plays vital role in our daily life. Noise is one of the important limitation in digital image processing. Noise is basically the type of unwanted disturbance in the image. A lot study is made so outlying to diminish or confiscate noise from digital images. Most of the filter fails when the noise density is very high. This paper proposes improved modified decision based median filter whic...

Median tilters operate by selecting the middle valve of an ascending-ordered scqucncc of numbers. These numbers are taken from a moving window on the data to be processed. The operation can be used in seismic data processing to reject glitches on data as well as enhance disconbnuiries in the dara. This paper reviews the definibons and some of the properties of median filters. The median filter ...

Weighted median filters (smoothers) have been shown to be analogous to normalized FIR linear filters constrained to have only positive weights. In this paper, it is shown that much like the mean is generalized to the rich class of linear FIR filters, the median can be generalized to a richer class of weighted median (WM) filters admitting positive and negative weights. The generalization follow...