Gaussian Blur: So, my method is based on Ivan Kutskir, Who has demonstrated Gaussian blur using Fast image convolutions by Wojciech Jarosz. According to Ivan, the convolution of two 2D functions f and g is defined as the volume of product of f and shifted ‘g’. which means that just by shifting one of the function of two, 2D functions blur can be achieved. Since it determines, how much of ‘f’ will get into the result. The Gaussian blur for a 2D function can be defined as a convolution of that function with 2D Gaussian. The Gaussian function is defined as a standard deviation also we are calling it as a ‘radius’. In discrete finite case, we represent our 2D functions as matrices of values. Now we calculate the volume as a sum. Since Gaussian blur, value is in negative (behind the zero) we will use only all the values between (-r,r) something like −r≤x≤r,−r≤y≤r. This part of Weight is also called kernel. The values are i and j and they are average be...