It also gave me a new perspective in image processing, as it helps bridge the Simulating effects that are now commonplace in modern cameras and smartphones. Overall, I thought it was interesting how sub-apertures within lightfield data could be combined in very simple ways to simulate changes in depth and aperture, Knight Aperture Adjustment (C=-0.175, r=0 to r=100) Summary Some examples of this are provided below for different C values. Different values of C will cause the camera's focus to change locations. shift every sub-apeture towards the center image), then average all the images together. In the lightfield by C * (u I - u c, v I - v c) (i.e. To refocus an image, we first shift every image I x, y We define the center image as I 8, 8 with a corresponding (u c, v c) location. depth refocusing).Įach lightfield dataset contains 289 sub-aperture images I x, y in a 17x17 grid (0-indexed) with corresponding (u, v) values to signify the camera's location. Generate images that focus on different objects at different depths (i.e. We can take advantage of this phenomenon to Images together without modifications generates a photo with nearby objects appearing blurry and far-away objects appearing sharp. Objects that are closer to the camera vary their positions significantly between images whereas objects that are farther don't vary as much. If we move the camera while keeping the lens' optical axis unchanged, This is a simple technique to make the image look slightly non realistic by minimising the gradient within the image and maximising the gradient on the edges.When generating lightfield data, we move the camera and have it capture photos from a variety of angles. This is a simple technique to highlight the vertical and horizontal edges of an image. Now, we combine each level of the corresponding pyramid to form an Image Pyramid of the final image which is then finally collapsed to get the final result. A gaussian pyramid for the mask is created. In this technique we create the laplacian pyramid of both the images that we need to blend together after aligning the source and background images. Here I aim to implement the laplacian pyramid blending technique. Here we implement gradient domain editing in order to preserve the details and yet finally convert a color image to grayscale. Sometimes when a colored image is converted to grayscale it loses some of the details. Rest of the procedure remains the same as Poisson blending. We use the gradient that is stronger in magnitude while forming the system of equation for solwing the least squares. In this technique, we instead of just considering the gradient of the source pixel, we also take into consideration the gradient of the background too. In this part of the project we implement another technique of seamless image blending through gradient domain editing. This results in blurred lines and improper blending due to which it appears fake. The failure case results due to the contrasting difference between the background of the source image and the target image. The resulting system of equations is then solved as a least squares problem which gives us the values of the mask region in the target image, which is then filled to get the output image. In the above equation the variable v represents pixel values from the target image, s represent the pixel values from the source region under the mask and t represents the pixel values of the neighbours of source pixel that lie at the boundary of the mask whose values are taken from the target image. For each pixel in the source region mask we solve the lease squares problem, For each pixel in the target image that falls outside the source region mask we copy the pixel values from the background image. Now, we need to make sure that all three images - source region, source mask and the background image are of the same size and are properly aligned. In this method we take two images a source image and a background image, we cut the portion of the source image that we want to blend into the background image and prepare a mask. Finally the solved pixel values are then placed in their respective positions in the target image to get the output. We try to solve for the pixels of the target image based on the system of equations prepared using the gradient condition and the system of equations is solved using the least squares method. This involves editing the the images in the gradient domain. This project aims at implementing the seamless image editing paper Perez, et al. Gradient Domain Editing Computational Photography Project #3
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