When k > 1, the process is referred to as highboost filtering. Choosing k < 1 de-emphasizes the contribution of the unsharp mask. When k = 1, we have unsharp masking, as defined above. Where we included a weight, k (k >= 0), for generality. Then we add a weighted portion of the mask back to the original image: Letting denote the blurred image, unsharp masking is expressed in equation form as follows. Subtract the blurred image from the original (the resulting difference is called the mask.).This process, called unsharp masking, consists of the following steps: In this blog post, we implement a classical spatial filter – spatial filtering deals with performing operations, such as image sharpening, by working in a neighborhood of every pixel in an image – called Highboost Filtering.Ī process that has been used for many years by the printing and publishing industry to sharpen images consists of subtracting an unsharp (smoothed) version of an image from the original image.
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