One Shot Keto Canada rocessed image g. It does not consider the pixel values of its neighboring locations. So in such cases we can write the transformation function in the form, s = T(r) , where this r is the pixel value in the original image and s is the pixel value in the corresponding location in the processed image. So this transformation function, it simply becomes of this form, s = T(r) , where s and r are independent pixel values at different locations. Now these transformation functions can be put in the form of these two figures. So in this particular case, the first figure shows a transformation function where you find that here in this case along the x axis or along the horizontal axis we have put the intensity values r of the original image and along the vertical axis we have put the intensity values of different pixels in the processed image g and obviously they are related by s = T(r) . And the transformation function is given by this particular curve. So this is our T(r). And in this particular figure as it is shown, that the point so the pixel values near 0 has been marked as dark regions. So it is quite obvious, that in an image, if the intensity values of the pixels are near about 0 that is very small intensity values, those regions appear as very dark and the intensity values which are higher in an image those regions appear as light regions. So this, first one, the first transformation function shows that in this particular range, a very narrow range of the intensity values in the original image is mapped to a wide range of intensity values in the processed image g. And effectively this is the operation which gives enhancement of the image. In the second figure that we have shown, here you find that this particular transformation function says that if I consider say this is some intensity value say I, so for all the pixels in the input image if the intensity values are less tha
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