Define the following with suitable examples
i) Unsharp marking
ii) High boost filtering
iii) High frequency filterin
Unsharp masking is a spatial domain image enhancement technique used to sharpen an image by emphasizing fine details and edges. The basic idea is to subtract a blurred (unsharp) version of the image from the original image to extract high-frequency components, and then add this detail back to the original. Mathematically, if the original image is denoted by f(x, y) and a blurred _______ ______ ____ _____ ___ ____ ________ _________ ____ ______.
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) Given an image f(x,y) with Fourier transform F(u,v) obtain the fourier transform of .
c) Given that the 2-D Fourier Tranform is real and even, obtain the constraints on the image characteristics.
Suppose a low-pass spatial filter is formed by averaging the four immediate neighbours of a point (x, y) but excluding the point itself. Find the equivalent filter H(u,v) in the frequency domain.
Explain the functioning of an adaptive, local noise reduction filter.
Given that
where f and g are real images and h is a spatial filter.
Obtain G(u,v), in terms of F(u,v), and H(u,v), the 2–D Fourier transform of g(x,y).
Filter the given gray level image with
i) mean filter using zero padding
ii) 3×3 weighted mean filter using zero padding with mask
iii) 3×3 median filter processing only such pixels that have all the needed neighbours.
iv) Laplacian filter with the given mask and reflecting the border pixels.
Show that Sobel masks can be implemented by one pass of differencing mask of the form [−1 0 1](or its vertical counterpart) followed by a smoothing mask of the form [1 2 1] (or its vertical counterpart).
Apply the perception algorithm to the following pattern classes:,
.
Let and
Also, Sketch the decision surface.