Research On The Methods Of Wavelet Transforms For CR Image Noise Filtering

With the development of computer technology and digital technology, digitized medicine technology has developed, while CR (computed radiography) is one of the typical methods.In the process of computed radiography, it is inevitable to bring many kinds of noise in the computed radiography images. So the images have been degraded, which can worsen the result and the analysis of doctors. So in order to improve the validity of the computed radiography images it is necessary to filter the noises. And before the filtering, we must firstly get cross where the noises come from and what the noises'character is and what kinds of relationship between the noise and the useful information from the images. Then we can do some pertinent filtering. Since different methods will have different effects of the filtering, we should base on the specialists of computed radiography and digital image processing to study filtering methods for computed radiography images noises.Wavelet transform is a new embranchment of mathematics based on Fourier's transform. It can combine time domain and frequency domain to describe the character of time-frequency of signals, and it also can depict the local feature of the signals in the time domain and frequency domain. At present, wavelet transform is widely used in voices, images, graphics, communication, earthquake, computer vision and so on. So it also can be useful in filtering the noises of CR images. Our major work:i. Firstly we introduce the equipment and the theory of the computer radiography system, analyses its character and imaging process, based on these, we know the source of the noises and the major two kinds of noises are Gaussian noise and Poisson noise. Then we discuss the relationship between the noises and the useful information in the images and distinguish them in some extension.ii. We introduce the basic theory of the wavelet transformation especially the Multi- resolution Analysis (MRA), These theories are bases of the filtering methods using wavelet transformation.iii. We analyze four different filtering methods using wavelet transformation and develop the method of using modulus maximum to make it facility. Then we compare these methods each other and with classical filtering methods. From these we can put forward that methods based on wavelet transform are better than classical filtering methods and the methods of modulus maximum and scale relativity are better. At last, we bring forward the application of methods.iv. Above all, we use all these methods to filter the Gaussian noise and Poisson noise. Then we use these methods on Poisson noise. Through the comparison and the analysis, we find that wavelet transformation filtering methods are still useful for images with Poisson noise, but the medial filtering is the best method for multiplicative Poisson noise.