![]() In this case, as opposed to the above formula, we need to subtract the intensity of each channel from 255. Those images contain three channels, red, green, and blue, referred to as RGB images. Nowadays, most of our images are color images. When we apply the image inverse operator on a grayscale image, the output pixel O(i,j) value will be: O(i,j) = 255 - I(i,j) To clarify a bit here, the intensity values in the grayscale image fall in the range, and (i,j) refers to the row and column values, respectively. Say that I(i,j) refers to the intensity value of the pixel located at (i,j). ![]() In other words, the dark areas become lighter, and the light areas become darker. So, let's get started! Image InverseĪs you might have guessed from the title of this section (which can also be referred to as image negation), image inverse aims to transform the dark intensities in the input image to bright intensities in the output image, and bright intensities in the input image to dark intensities in the output image. We'll have a look at how we can implement them in Python. There are many techniques for image enhancement, but I will be covering two techniques in this tutorial: image inverse and power law transformation. Image enhancement is usually used as a preprocessing step in the fundamental steps involved in digital image processing (i.e. So here we actually don't know what the output image would look like, but we should be able to tell (subjectively) whether there were any improvements or not, like observing more details in the output image, for instance. It is important to note that image enhancement does not increase the information content of the image, but rather increases the dynamic range of the chosen features, eventually increasing the image's quality. When we enhance an image, what we are doing is sharpening the image features such as its contrast and edges. We thus need a way to improve the quality of output images so they can be visually more expressive for the viewer, and this is where image enhancement comes into play. When we talk about image enhancement, this basically means that we want a new version of the image that is more suitable than the original one.įor instance, when you scan a document, the output image might have a lower quality than the original input image. Let me start this tutorial by taking some theoretical jargon out of your way.
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