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Application of the decision-based/adaptive median filter (Section 2.2).

The proposed strategy can be briefly summarized as follows: 1. Application of the decision-based/adaptive median filter (Section 2.2).

In our research, we have used the following setup of the decision-based/adaptive median filter described in Section 2.2.

This paper adopts a similar philosophy, restricting consideration to a class of nonlinear filters obtained by combining two previously studied filter classes: the Hampel filter described in Section 2, and the median filter extensions described in Sections 4 and 7.

In the following section, the median filter for removing transient noise is briefly described.

As noted in Section 2, the Hampel filter reduces to the standard median filter when the threshold parameter has the value t=0, and it becomes generally less aggressive with increasing t.

Section 3 describes proposed impulse noise detection algorithm, proposed dynamically weighted median filter, and proposed adaptive dynamically weighted median filter (AMWMF).

In this section, we firstly propose the transient noise reduction system based on the median filter which utilizes the LTP as a pre-processor.

As with the previous approach (Section 3.4) input images to the weight calculation step are pre-filtered using a median filter.

Specifically, the maximum possible frequency is that of the binary implosion sequence described in Section 3, implying that in this limit, the Hampel filter offers no advantage over the median filter.

The Hampel filter introduced in Section 2 is effectively a moving window outlier detector that replaces the original signal value with the median filter response if that value is deemed an outlier.