DIGITAL IMAGE PROCESSING MINI-PROJECT
IMAGE DENOISING AND FEATURE EXTRACTION USING SPATIAL FILTERS
I would like to thank Prof. Thanikaiselvan Sir for constantly guiding me through the course of this project. His class lectures made my concepts clear in image processing and made me familiar with the various image processing techniques and the various operations that can be performed on images. This helped me implement my desired task using MATLAB and so I was able to complete my mini-project on “Image denoising and feature extraction using spatial filters” successfully.
In image processing, the quality of any image gets badly corrupted by noise whether it be any kind of noise. To combat this problem of noise, we need to improve the overall system quality. We can implement various image denoising techniques to reduce the noise. We are doing image denoising in spatial domain here. In spatial domain, operations are performed on the pixels itself. We have implemented three different kinds of filter: median filter, averaging filter and wiener filter to denoise the image and then have analyzed the results. Also we have implemented 2-D Gaborfilter for feature extractions of an image.
To remove various types of noise and also do feature extraction using different types of spatial filters. OBJECTIVE:
* To perform image denoising using three spatial filters- Median filter, averaging filter and Wiener filter. * To extract particular features of an image using another kind of spatial filter- Gabor filter and so study the different applications of spatial filters THEORY:
Image denoising is the first preprocessing step in dealing with image processing where the overall system quality needs to be improved. Generally, the quality of an image gets corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. The great challenge of image denoising is how to preserve the edges and all fine details of an image when reducing the noise. Noise occurs during image capture, transmission or processing phases. The noise contaminated in any image could be dependent on or independent of image content. Whatever the classification of the noise as image dependent or independent, the removal of the undesired pattern stay as a great challenge in several vital applications. The noise is characterized by its pattern and by its probabilistic characteristics. There is a wide variety of noise types: Gaussian noise, speckle noise, poisson noise, impulse noise, salt and pepper noise. The denoising filters could be applied as a local window in the neighborhood region or holistic to the whole image. The robustness of image improvement techniques could be achieved by suppressing the noise while preserving the characterizing features of the image.
We will be using spatial filters here for removing different kinds of noises. In spatial filtering, filtering operations are performed directly on the pixels of an image. We will be using two types of spatial filters here for removing two different types of noises: 1. MEDIAN filter and AVERAGING filter for removing salt and pepper noise 2. WIENER filter for removing Gaussian noise.
Salt and pepper noise is a form of noise which is typically seen on image and it represents itself as randomly occurring white and black pixels. Salt and pepper noise affects the images in situations where quick transients, such as faulty switching, take place. Gaussian noise is statistical noise that has its probability density function equal to that of the normal distribution. When an electrical variation obeys a Gaussian distribution, such as in the case of thermal motion, it is called...
References: 1. Digital Image Processing by Gonzalez
2. An introduction to Digital Image Processing with Matlab Notes for SCM2511 Image Processsing1 by Alasdair McAndrew
3. Wiener Filtering and Image Processing. www.clear.rice.edu/elec431/projects95/lords/wiener.html
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