The images obtain from either research studies or optical instruments are
often corrupted with noise. Image denoising involves the manipulation of image
data to produce a visually high quality image. This thesis reviews the existing
denoising algorithms and the filtering approaches available for enhancing images
and/or data transmission.
Spatial-domain and Transform-domain digital image filtering algorithms
have been used in the past to suppress different noise models. The different noise
models can be either additive or multiplicative. Selection of the denoising algorithm
is application dependent. It is necessary to have knowledge about the noise present
in the image so as to select the appropriated denoising algorithm. Noise models
may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian
noise. The Wavelet Transform is similar to the Fourier transform with a completely
different merit function. The main difference between Wavelet transform and
Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both
time and frequency. In the standard Fourier Transform, Wavelets are only localized
in frequency. Wavelet analysis consists of breaking up the signal into shifted and
scales versions of the original (or mother) Wavelet. The Wiener Filter (mean
squared estimation error) finds implementations as a LMS filter (least mean
squares), RLS filter (recursive least squares), or Kalman filter.
Quantitative measure (metrics) of the comparison of the denoising algorithms
is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square
Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A
combination of metrics including the PSNR, MSE, and MAE are often required to
clearly assess the model performance
