I need a matlab code to implement tne rest of the project without using any of the matlab image processing toolbox or presettings.

# count the new grey level in position(i,j)
	def spatial_filtering(i, j):
	# create a s*s array to represent the pixels that have
	# influence on position(i,j)'s new grey level
	array = [[(0,0)] * s ] * s
	average = 0
	# count the coordinate of the s*s pixels
	for a in range(s):
	for b in range(s):
	x = i + dis[a]
	y = j + dis[b]
	# if the pixel's position is out of the image's range
	# set it to the boundary's value
	if(x
	x = 0
	if(y
	y = 0
	if(x >= width):
	x = width - 1
	if(y >= height):
	y = height - 1
	# keep the array for better understanding, it can be deleted
	array[a][b] = ( x, y )
	#count the average of the s*s pixels' grey level
	average += data[array[a][b]]
	average = average / (s * s)
	#return average
	return average
	# open an image and get its information
	im = Image.open('Fig0219(rose1024).tif')
	data = im.load()
	width, height = im.size
	MN = width * height
	# new two blank images for rewrite and downsampling
	resultImage = Image.new('L',(width, height), 'white')
	# count the frequency of each grey level
	s = input('please input size: ')
	dis = [0] * s
	for c in range(s):
	dis[c] = c - (s / 2)
	# draw the new HE image
	draw = ImageDraw.Draw(resultImage)
	#width = height = 10
	for i in range(width):
	for j in range(height):
	# for each pixel, find its corresponding grey level after HE
	p = spatial_filtering(i, j)
	draw.point((i, j), p)
	filename = 'SFImage_rose_blursize=' + str(s) + '.bmp'
	#save the output files
	resultImage.save(filename, format='BMP')

PROJECT 4- SPATIAL DOMAIN IMAGE PROCESSING Spatial Filtering Write program to perform spatial filtering of an image (see Section 3.4 regarding implementation). You can fix the size ofthe spatial mask at 3x3, but the coefficients need to be variables that can be input into yourprogram. This project is generic, in the sense that it will be used in other projects to fo llow Enhancement Using the Laplacian (a) Use the programs developed above (and in Project 2)to implement the Laplacian enh ancement technique descrbed in connection with Eq. (3.6-6). Use the mask shown in Fig. 3.37(d). (b)Duplicate the results in Fig. 3.38. You will need to download Fig0338(a)(blurry moon). Unsharp Masking (a) Use the programs developed above (and in Project 2)to implement high-boost filtering, as given in Eqs. (3.6-8,3.6-9). The averaging part of the process should be done using the mask in Fig. 3.32 (a). (b)Download Fig0340(a)(dipxe text) an d enhance it using the program you developed in (a). Your objective is to choose constant k so that your result visually approximates Fig. 3.40(e). PROJECT 4- SPATIAL DOMAIN IMAGE PROCESSING Spatial Filtering Write program to perform spatial filtering of an image (see Section 3.4 regarding implementation). You can fix the size ofthe spatial mask at 3x3, but the coefficients need to be variables that can be input into yourprogram. This project is generic, in the sense that it will be used in other projects to fo llow Enhancement Using the Laplacian (a) Use the programs developed above (and in Project 2)to implement the Laplacian enh ancement technique descrbed in connection with Eq. (3.6-6). Use the mask shown in Fig. 3.37(d). (b)Duplicate the results in Fig. 3.38. You will need to download Fig0338(a)(blurry moon). Unsharp Masking (a) Use the programs developed above (and in Project 2)to implement high-boost filtering, as given in Eqs. (3.6-8,3.6-9). The averaging part of the process should be done using the mask in Fig. 3.32 (a). (b)Download Fig0340(a)(dipxe text) an d enhance it using the program you developed in (a). Your objective is to choose constant k so that your result visually approximates Fig. 3.40(e)