def mp_a_trous(C0, wavelet_filter, scale, core_count):
    """
    This is a reimplementation of the a trous filter which makes use of multiprocessing. In particular,
    it divides the input array of dimensions NxN into M smaller arrays of dimensions (N/M)xN, where M is the
    number of cores which are to be used.

    INPUTS:
    C0              (no default):   The current array which is to be decomposed.
    wavelet_filter  (no default):   The filter-bank which is applied to the components of the transform.
    scale           (no default):   The scale at which decomposition is to be carried out.
    core_count      (no default):   The number of CPU cores over which the task should be divided.

    OUTPUTS:
    shared_array                    The decomposed array.
    """

    # Creates an array which may be accessed by multiple processes.

    shared_array_base = mp.Array(ctypes.c_float, C0.shape[0]**2, lock=False)
    shared_array = np.frombuffer(shared_array_base, dtype=ctypes.c_float)
    shared_array = shared_array.reshape(C0.shape)
    shared_array[:,:] = C0

    # Division of the problem and allocation of processes to cores.

    processes = []

    for i in range(core_count):
        process = mp.Process(target = mp_a_trous_kernel, args = (shared_array, wavelet_filter, scale, i,
                                                     C0.shape[0]//core_count, 'row',))
        process.start()
        processes.append(process)

    for i in processes:
        i.join()

    processes = []

    for i in range(core_count):
        process = mp.Process(target = mp_a_trous_kernel, args = (shared_array, wavelet_filter, scale, i,
                                                     C0.shape[1]//core_count, 'col',))
        process.start()
        processes.append(process)

    for i in processes:
        i.join()

    return shared_array
This is a reimplementation of the a trous filter which makes use of multiprocessing. In particular,
    it divides the input array of dimensions NxN into M smaller arrays of dimensions (N/M)xN, where M is the
    number of cores which are to be used.

    INPUTS:
    C0              (no default):   The current array which is to be decomposed.
    wavelet_filter  (no default):   The filter-bank which is applied to the components of the transform.
    scale           (no default):   The scale at which decomposition is to be carried out.
    core_count      (no default):   The number of CPU cores over which the task should be divided.

    OUTPUTS:
    shared_array                    The decomposed array.