Explain a Model of the Image Degradation/Restoration Process.
The Fig. 6.3 shows, the degradation process is modeled as a degradation function that, together with an additive noise term, operates on an input image f(x, y) to produce a degraded image g(x, y). Given g(x, y), some knowledge about the degradation function H, and some knowledge about the additive noise term η(x, y), the objective of restoration is to obtain an estimate f(x, y) of the original image. the estimate should be as close as possible to the original input image and, in general, the more we know about H and η, the closer f(x, y) will be to f(x, y).
The degraded image is given in the spatial domain by
g (x, y) = h (x, y) * f (x, y) + η (x, y)
where h (x, y) is the spatial representation of the degradation function and, the symbol * indicates convolution. Convolution in the spatial domain is equal to multiplication in the frequency domain, hence
G (u, v) = H (u, v) F (u, v) + N (u, v)
where the terms in capital letters are the Fourier transforms of the corresponding terms in above equation.
Fig. 6.3 model of the image degradation/restoration process.
