Develop a Program that will implement the non-linear filters
Abstract:
The purpose of this project is to develop a program that implements non-linear filters. For this project we will research the mean filter and the Median filter.
Introduction:
The Idea of this project is to generate and image and implement different types of noise, then add them together and run them through a non-linear filter and see how the filter affects the output image. First we must locate and image then add the noise and run the image thru a non-linear filter to successfully remove all sort of noise corruption.
We will compare two filters, the mean filter and the median filter, for a few simple cases. The purpose of the filtering operation is assumed to be an effective elimination or attenuation of the noise that is corrupting the desired images. In this report we will consider only the two-dimensional cases (image). The effects are better visualized with images.
Background on non-linear filters:
Non-linear filtering has been considered even in the fifties, since then, the field has seen a rapid increase of interest indicated. In our case the Multistage medians and median filters have been rather extensively studied from the theoretical point of view in the beginning of the seventies in the Soviet Union. These filters have been independently reinvented and put into wide practical use around 15 years later by western researchers.
Non-linear FIR filters cannot be expressed as a linear combination of the input, but as some other (non-linear) function on the inputs. A simple example of a useful non-linear filter is a 5th order median filter. This is the filter represented by:
This type of filter is extremely useful for data with non-Gaussian noise, removing outliers very efficiently. A significant amount of research effort has gone into the development of appropriate filters for various purposes.
Statistics has taken a different tack to the problem: early approaches were similar to moving average filters. However, rather than using a simple moving average, the early work realized that linear regression could be used around the point we were trying to estimate; in other words, rather than simply averaging the five values around a point, a linear fit of the points, using a least squares estimate, could be used to give a better-looking result. Furthermore, we realized that
1) Linear regression could be applied, so could other shapes, in particular splints.
2) The weights for the instances used in regression could be changed.
Optical monochrome filters are used to filter out all the wavelengths except desired one. Since we are interested in Green, Red and Near Infrared wavelengths the following filters are used.
For combining the profit of PCA and wavelets, the capacity for each variable are decomposed to its wavelet coefficients by the same wavelet for each variable. This transformation of the data matrix brings X into a matrix, WX, while W is an n * n orthonormal matrix showing the orthonormal wavelet transformation operator that contains the filter coefficients.
The histogram of an input image is computed for selection of threshold value of a converted gray image. MATLABs ‘imhist(…)’ is the function that is used generate histogram. The appropriate threshold value has been selected, which is, then, applied to an image to threshold itself. Fig 8 and Fig 9 show an example of such images.
the mean value and the standard deviation, to represent the global characteristics of the image, and the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. Aptoula et al. [8] presen...
One of the most widely known filter and successfully applied in practical engineering field is Kalman filter, which appeared in early 1960. Kalman filter, basically based on simple approach that the noise has a Gaussian characteristics, measurement and inherent system noise are not correlated, and the system is in a linear dynamics. A more general formulation of filtering problems have been proposed in late 1960, to name few, Strantonovic, Zakai, Kushner and Mortensen. These filtration formulation...
Essentially, once an image exists in digital form, it can either be tweaked to adjust even its most indiscernible features or it can be entirely morphed into something altogether different. There ...
In this section, the results of the research are presented. For each task carried out, the most important information obtained is presented.
I would eliminate the outlier if I need to get a better line of best
The relation of the filter to the system is illustrated in the block diagram of figure 16. The basic steps of the computational procedure for the discrete-time Kalman estimator are as follows:
As the camera zoom smoothly creeps in from the establishing wide we are exposed to a changing palette of noises from the surrounding environme...
Image segmentation divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Famous techniques of image segmentation which are still being used by the researchers are Edge Detection, Threshold, Histogram, Region based methods, and Watershed Transformation.
For the second part of the assignment, we make a window of user defined size around the centre pixel under consideration, calculate the average value for all the pixels in this window and then binarise that centre pixel using this average value as the threshold value. We continue this procedure till we binarise the whole image.
In his results, he documented that the profitable signal for Moving Average was 52.62% and that the new algorithm have a profitable signal of approximately 5...
One small thing can change a picture entirely. This one small thing is a camera filter. A camera filter is a small round attachment that goes in front of the camera lens. The camera filter was invented by Edwin H. Land. In this experiment, the polarizing filter, neutral density filter, diffusion filter, and star effect Filter will be tested. Photographers everywhere use filters to help enhance their photos. The hypothesis states that if a filter is applied to the camera, then the picture will change.
Figure 2.1 This figure is shows the sampling data of image aerial photograph. (Norbert Haala, 2009)