Function Index
	DiscreteConvolve DiscreteConvolve[img, h] returns the circular convolution of img and filter h. The option Circular→True (the default) returns a circular convolution result that has the same dimensions as img. The option Circular→False returns a linear convolution result. The dimensions of the result are larger than the dimensions of original data. Assuming img is of dimensions Nr × Nc and h is of dimensions Mr × Mc, the returned result is of dimensions Nr+Mr-1 × Nc+Mc-1. TransientSamples → False returns the steady-state samples only. The option Centered positions the steady-state output values in the output array. The option Centered only takes effect if Circular → True. The option Centered → False places all the transient samples at the beginning of the output array. Specifically, element {1,1,...} of the result contains img1,1,... h1,1,..., and element {-1,-1,...} of the result contains img-1,-1,... h1,1,.... The option Centered → True (default) evenly distributes the transient samples at the beginning and end of the output array. Specifically, element {1,1,...} of the result contains , and element {-1,-1,...} of the result contains . The argument img may be a vector, matrix, or ImageData expression. The argument ker may be a vector or matrix. The TensorRank of ker and the raw image data in img must be equal. See also User's Guide 5.2. Examples This loads the package. In[1]:= This defines a 1D signal sig and an impulse response ker. In[2]:= This returns a circular convolution result. In[3]:= Out[3]//TableForm= This returns a linear convolution result. In[4]:= Out[4]//TableForm= This returns the steady-state values only (note that this result is independent of option Circular). In[5]:= Out[5]//TableForm= Here we blur a picture by convolving with a moving average filter. First we define the filter and load an example image. In[6]:= In[7]:= Here we display the original and blurred images. In[8]:=