X contains the coefficients of each output pixel in X. A is same across all color channels hence we create is only once, but we calculate three different B column vectors: one for each color channel. Once we have the coefficient matrix A and B, we can find our output image.ī is a (Nx1), represents the desired output gradient and values, and A is a large, sparse matrix define as (NxN), represents unknown gradient values. We go through the entire image and create a vector with one entry for each pixel which takes the actual values of either the target image, if the mask was 0 at that point, or the gradient value from the source if the mask was 1 at that point. If it is 1, we want to set up a linear equation such that the gradient for a given pixel is the same in both the source and final images. In our final image, if the mask's value is 0, we want to just take the target's pixel value at that point. We have a mask which has the same size as both the source and target images. The image blending problem is phrased as a least-squares problem, which requires solving AX = B for every pixel under the mask. The result is that each "transplanted" pixel on the target image has a gradient that is the same as the gradient of the corresponding pixel on the source image, but still have all the features of the source image. Therefore, we compute information about the gradient for each pixel in that part of the source image, and then apply the same gradient in the target image. Given that people often care mush more about the gradient of an image than the overall intensity. A simple cut and paste of the masked region gives a bad blend since the gradients of the source do not match the gradients of the target. Given a source image, a target image and a "mask" image to determine which pixels should blend from the source image into the target image. ![]() This project implements the seamless image composition algorithm from Pérez, et al."Poisson Image Editing".
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