The filter changes its smoothing capability depending on the CFA color of the current pixel and its similarity with the neighborhood pixels.More specifically, in relation to image content, the following assumptions are considered:- if the local area is homogeneous, then it can be heavily filtered because pixel variations are basically caused by random noise.- if the local area is textured, then it must be lightly filtered because pixel variations are mainly caused by texture and by noise to a lesser extent; hence only the little differences can be safely filtered, as they are masked by the local texture.3.?The Proposed Technique3.1. Overall filter block diagramA block diagram describing the overall filtering process is illustrated in Figure 2. Each block will be separately described in detail in the following sections.

Figure 2.Overall Filter Block Diagram.The fundamental blocks of the algorithm are:Signal Analyzer Block: computes a filter parameter incorporating the effects of human visual system response and signal intensity in the filter mask.Texture Degree Analyzer: determines the amount of texture in the filter mask using information from the Signal Analyzer Block.Noise Level Estimator: estimates the noise level in the filter mask taking into account the texture degree.Similarity Thresholds Block: computes the fuzzy thresholds that are used to determine the weighting coefficients for the neighborhood of the central pixel.Weights Computation Block: uses the coefficients computed by the Similarity Thresholds Block and assigns a weight to each neighborhood pixel, representing the degree of similarity between pixel pairs.

Filter Block: actually computes the filter output.The data in the filter mask passes through the Signal Analyzer block that influences the filter strength in dark and bright regions (Section 3.2 for further details). The HVS value is used in combination with the output of the Texture Degree Analyzer (Section 3.4) and Noise Level Estimator (Section 3.5) to produce the similarity thresholds used to finally compute the weights assigned to the neighborhood of the central pixel (Section 3.6). The final filtered value is obtained by a weighted averaging process (Section 3.7).3.2. Signal Analyzer BlockAs noted [31�C33], it is possible to approximate the minimum intensity gap that is necessary for the eye to perceive a change in pixel values.

The base sensitivity thresholds measure the contrast sensitivity in function of frequency while fixing the background intensity level. In general, the detection threshold varies also with the background intensity. Entinostat This phenomenon is known as luminance masking or light adaptation. Higher gap in intensity is needed to perceive a visual difference in very dark areas, whereas for mid and high pixel intensities a small difference in value between adjacent pixels is more easily perceived by the eye [32].