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Study Of Gaussian & Impulsive Noise Suppression Schemes In Images


Academic year: 2023

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This is to certify that the work in the thesis titled Study Of Gaussian & Impulsive Noise Suppression Schemes In Images by Ravi Karan Sharma & Rohit Nibariya is a record of an original research work carried out by them under my supervision and guidance in part fulfillment of the requirements for the award of the degree of Bachelor of Technology in Computer Science and Engineering during the session 2005–. Also for the resources and facilities that were available to us whenever we needed, this was an important part of the success of this work. The main type of noise added during image acquisition is called Gaussian noise, while impulsive noise is generally introduced while transmitting image data over an insecure communication channel, while it can also be added through acquisition.

Different techniques are used to remove these types of noises based on the properties of their respective noise patterns. In this method, the difference of the weighted mean at the center of a neighborhood with the central pixel under consideration is compared to a set of thresholds. Another method is implemented which takes into account the presence of noise-free pixels. It interpolates the median of each neighborhood with a set of convolution kernels which are oriented according to all possible edge configurations containing the center pixel, if it lies on an edge.

A third method is implemented, which deals with the detection of noisy pixels on binary image slices. The filter inverts the value of the center pixel if the number of pixels with values ​​opposite it is greater than the threshold. The fourth method has an efficient double-lead detector that gives a decision based on the double-lead value.

Gaussian denoising algorithms should ideally smooth separate parts of an image without blurring the edges. A universal denoising scheme is implemented.

Image Processing

For the first seven functions, the input and output are images, while the output, as for the other three, are attributes from the input images. With the exception of image capture and display, most image processing functions are implemented in software. Imaging is characterized by specific solutions, so the technique that works well in one area may be inadequate in another.

From the ten sub-branches of digital image processing, cited above, this thesis deals with image restoration.

Noise In Image Processing

The standard model of amplifier noise is additive, Gaussian, independent at each pixel and independent of signal intensity, caused primarily by Johnson-Nyquist noise (thermal noise), including noise originating from capacitor reset noise ("kTC noise"). color cameras that use more gain in the blue color channel than the green or red channel may have more noise in the blue channel. Amplifier noise is an important part of an image sensor's "read noise", that is, of the constant noise level in dark areas of the image.[9]. The noise caused by quantizing the pixels of a detected image to a number of discrete levels is known as quantization noise; it has an approximately uniform distribution and can be signal dependent, although it will be signal independent if other noise sources are large enough to cause dithering, or if dithering is explicitly applied.

The grain of photographic film is a signal-dependent noise, related to shot noise. That is, if film grains are uniformly distributed (equal number per area), and if each grain has an equal and independent probability of developing into a dark silver grain after absorbing photons, the number of such dark grains in a be area random with a binomial distribution; in areas where the probability is low, this distribution will be close to the classical Poisson distribution of shot noise; nevertheless, a simple Gaussian distribution is often used as an accurate enough model. Film grain is usually considered a nearly isotropic (non-oriented) noise source, and is exacerbated by the fact that the distribution of silver halide grains in the film is also random. For example, image sensors are sometimes subject to row noise or column noise. In film, scratches are an example of non-isotropic noise.

Noise cannot be removed without loss of information in the form of image details.

Problem Definition


Thesis Organisation


Let Xij and Yij represent the pixel values ​​at position (i,j) in the corrupted and restored images, respectively. The standard median filter outputs the median value of the samples in the (2N+1) * (2N+1) window centered at Xij.

Adaptive Impulse Detection Using Center-Weighted Median Filters . 8

Take the difference between the median and the center value of the pixels, let's determine the differences.

Impulsive Noise Removal Using Threshold Boolean Filtering Based on


An attractive way to implement denoising is to divide the image into binary slices, further process these slices as binary images and fuse the processing results into the resulting image. Only the filtering of binary images is reduced to their processing using some logical values ​​of the function [7]. The following logic function was proposed to detect and remove pulses from binary images in:. Compare the value of the center element of x5 windows with other signal values ​​from the same window, the value of the center element is finally replaced by the median.

The procedure is similar to stack filtering, the value of the central element is finally replaced by the median and a threshold parameter is used. Based on the impulse detection functions. and∨ are the signs of conjunction and disjunction respectively. The function inverts the value of the central pixel of a 3X3 window, if the number of pixels with values ​​opposite to , exceeds , otherwise kept.

To do this, the 3X3 and 5X5 levels around the central pixel should be considered separately, because this allows for more careful noise detection.

Efficient Filtering Of Image Data Corrupted by Impulse Noise


Here n is the number of healthy pixels in the immediate vicinity of the test pixel, which is detected by counting the number of ones in the f matrix. Two noise models can adequately represent most of the noise added to images: additive Gaussian noise and impulsive noise. Additive Gaussian noise is characterized by adding to each image pixel a value from a zero-mean Gaussian distribution. Such noise is usually introduced during image acquisition. The zero-mean property of the distribution allows such noise to be removed from the local average of pixel values.

Ideally, removing Gaussian noise would involve smoothing out the different areas of an image without affecting the sharpness of their edges. A local image statistic is introduced for identifying noise pixels in images damaged by impulse noise of arbitrary values. The statistical values ​​quantify how different in intensity the specific pixels are from their closest neighbors. This statistic can be incorporated into a filter designed to remove additive Gaussian noise [9].

The result is a new filter that can effectively reduce both Gaussian and impulse noise from noisy images. The approach is extended to automatically remove any mixture of Gaussian and impulse noise. Calculate the spatial and radiometric weight of each pixel Let x be the location of the considered pixel , and let . Image with Gaussian and Salt & Pepper noise. a) First image with mixed noise with variance = 0.01 and impulse probability.

The adaptive impulse detection using center-weighted median filters is a typical median-based filter that produces the corrected image with the sharpest edges of all the algorithms discussed in this work. The corrected image at higher impulse noise of p=20% did not completely remove the noise. A new filter pass on the corrected image will be helpful to achieve better results.

But there are no obvious spots of impulse values ​​in the corrected image, even at higher values ​​of p, e.g. The algorithm for efficiently filtering image data damaged by Impulse noise serves its purpose and calculates the recovered pixel values ​​the fastest of all the algorithms discussed here. The universal noise removal algorithm with a pulse detector tackles both Gaussian and Boolean noise.

The corrected images produced by this algorithm are the most blurred of all the other implemented algorithms. We can try to implement and/or modify the existing algorithm so that they can smooth distinct regions of the image without blurring the edges and improve their spatial and temporal complexity by implementing them more efficiently.


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