Task 1: You will consider the effects of additive noise and the use of DFT to remove this kind of noise. The noisy image has been generated by adding noise in the form of a cosine function. Using frequency domain filtering, devise a procedure for removing the noise and show your results. For comparison purposes, remove the noise in the spatial domain by convolving the noisy image with Average filter (e.g., 7 x 7 and 15 x 15). Compare resulting images of both spatial and frequency domain filtering. Also, you can elaborate on differences in the jupyter notebook as a comment.
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- Write a program to reduce the number of gray levels L in a PGM image from L=256 to: (i) L=128,(ii) L=32, (iii) L=8, and (iv) L=2. Show your results using the “lenna” and “peppers” images. Forcomparison purposes, subsample the gray-levels to better visualize the quantized images (e.g.,if L=128, you can use 0, 2, 4, …, 254 with Q=255 in the PGM file instead of 0, 1, 2, …, 127 withQ=127; similarly, if L=2, you can use 0, 127 with Q=255 instead of 0, 1 with Q=1).Q1 Suppose an image has a line having three points with (x,y) coordinates (1,2), (2,3), and (3,4) (assume the image coordinates starts from (0,0)). What will these points be in the Hough space (m, b)? Draw them in Hough space with exact coordinates mentioned, where (m, b) are the parameters representing the slope and intercept of the line. What information do you get from the resultant Hough space?To animate an object while making the movements look realistic, we need to take care of acceleration and deceleration. With nonlinear interpolation, we can do that by using varying time steps using constant time steps fixing the number of inter between frames reducing the number of inter between frames
- Computer Science For an image represented by a given large 3d numpy array, use numpy to get a neighborhood filter using the mean of the pixels. For example, look through each of the image’s 3×3 window of pixels and replace outlier pixels with the mean value of the others in the 3×3 window. Set the resulting pixel value to be the mean value of a pixel and its eight neighbors. For pixels on an edge, use the mean value of the pixel and its five neighbors. For pixels on corners, use the mean value of the pixel and its three neighbors. If you loop over every pixel then you must store your input and output arrays separatelySuppose images f(x,y) and g(x,y) have histograms h; and hg. Suppose you form image k(x,y) as the sum of these two images: k(x,y) = f(x,y) + g(x,y). (a) Under what conditions (on images f and g) can you determine the histogram hų of image k in terms of the histograms hf and hg? (That is, under what conditions can you determine hu if you are only given hj and he? Hint: this turns out to be fairly restrictive.) (b) Explain how you would derive hỵ in this case.The edge-detection function (detectEdges) described in Chapter 7 and shown below returns a black and white image. Think of a similar way to transform color values so that the new image is still in its original colors but the outlines within it are merely sharpened. Then, define a function named "sharpen" that performs this operation. The function should expect an image and two integers as arguments. One integer should represent the degree to which the image should be sharpened. The other integer should represent the threshold used to detect edges. Code: from images import Image def sharpen(image, degree, threshold): """Builds and returns a sharpened image. Expects an image and two integers (the degree to which the image should be sharpened and the threshold used to detect edges) as arguments.""" pass def main(): filename = input("Enter the image file name: ") image = Image(filename) newimage = sharpen(image, 20, 15) newimage.draw() if __name__ == "__main__": main() Please only…
- Correct answer will be upvoted else downvoted. Computer science. You and your companions live in n houses. Each house is situated on a 2D plane, in a point with integer organizes. There may be various houses situated in a similar point. The chairman of the city is requesting you for places for the structure from the Eastern show. You need to track down the number of spots (focuses with integer arranges), so the outline distance from every one of the houses to the show is insignificant. The display can be inherent a similar point as some house. The distance between two focuses (x1,y1) and (x2,y2) is |x1−x2|+|y1−y2|, where |x| is the outright worth of x. Input First line contains a solitary integer t (1≤t≤1000) — the number of experiments. The principal line of each experiment contains a solitary integer n (1≤n≤1000). Next n lines portray the places of the houses (xi,yi) (0≤xi,yi≤109). It's reliable that the amount of everything n doesn't surpass 1000. Output For…I. Write/modify MATLAB code and create a properly labeled graph. Add 2 additional lines to your plot, “Cannon Range”, for v3 = 25 m/s and v4= 75 m/s..Write a pseudo code to implement the ray- tracing with BVH optimization (one ray per pixel and no sampling for diffuse).
- Although the plot function is designed primarily for plotting standard xy graphs, it can be adapted for other kinds of plotting as well. b. Make a plot of the curve, which is defined parametrically by the equations x = 2cosθ + cos2θ, y = 2sinθ - sin2θ, where 0 < θ < 2π. Take a set of values of θ between zero and 2π and calculate x and y for each from the equations above, then plot y as a function of x. b. Taking this approach a step further, one can make a polar plot r = f(θ) for some function f by calculating r for a range of values of θ and then converting r and θ to Cartesian coordinates using the standard equations x = r cosθ, y = r sinθ. Use this method to make a plot of the function r = ecosθ – 2 cos(4θ) + sin5 (θ/12) in the range 0 <= θ <= 24π. use python code to answer the highlight one2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises…subject : smart computing To animate an object while making the movements look realistic, we need to take care of acceleration and deceleration. With nonlinear interpolation, we can do that by using varying time steps using constant time steps fixing the number of inter between frames reducing the number of inter between frames