HDL Code Generation for Adaptive Median Filter

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This example shows how to generate HDL code from a MATLAB® design that implements an adaptive median filter algorithm and generates HDL code.

Adaptive Filter MATLAB Design

An adaptive median filter performs spatial processing to reduce noise in an image. The filter compares each pixel in the image to the surrounding pixels. If one of the pixel values differ significantly from the majority of the surrounding pixels, the pixel is treated as noise. The filtering algorithm then replaces the noise pixel by the median values of the surrounding pixels. This process repeats until all noise pixels in the image are removed.

design_name = 'mlhdlc_median_filter';
testbench_name = 'mlhdlc_median_filter_tb';

Review the MATLAB design:

edit(design_name);

%#codegen
function [pixel_val, pixel_valid] = mlhdlc_median_filter(c_data, c_idx)
%   Copyright 2011-2019 The MathWorks, Inc.

smax = 9;
persistent window;
if isempty(window)
    window = zeros(smax, smax);
end

cp = ceil(smax/2); % center pixel;

w3 = -1:1;
w5 = -2:2;
w7 = -3:3;
w9 = -4:4;

r3 = cp + w3;      % 3x3 window
r5 = cp + w5;      % 5x5 window
r7 = cp + w7;      % 7x7 window
r9 = cp + w9;      % 9x9 window

d3x3 = window(r3, r3);
d5x5 = window(r5, r5);
d7x7 = window(r7, r7);
d9x9 = window(r9, r9);

center_pixel = window(cp, cp);


% use 1D filter for 3x3 region
outbuf = get_median_1d(d3x3(:)');
[min3, med3, max3] = getMinMaxMed_1d(outbuf);

% use 2D filter for 5x5 region
outbuf = get_median_2d(d5x5);
[min5, med5, max5] = getMinMaxMed_2d(outbuf);

% use 2D filter for 7x7 region
outbuf = get_median_2d(d7x7);
[min7, med7, max7] = getMinMaxMed_2d(outbuf);

% use 2D filter for 9x9 region
outbuf = get_median_2d(d9x9);
[min9, med9, max9] = getMinMaxMed_2d(outbuf);


pixel_val = get_new_pixel(min3, med3, max3, ...
    min5, med5, max5, ...
    min7, med7, max7, ...
    min9, med9, max9, ...
    center_pixel);


% we need to wait until 9 cycles for the buffer to fill up
% output is not valid every time we start from col1 for 9 cycles.
persistent datavalid
if isempty(datavalid)
    datavalid = false;
end
pixel_valid = datavalid;
datavalid = (c_idx >= smax);


% build the 9x9 buffer
window(:,2:smax) = window(:,1:smax-1);
window(:,1) = c_data;

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [min, med, max] = getMinMaxMed_1d(inbuf)

max = inbuf(1);
med = inbuf(ceil(numel(inbuf)/2));
min = inbuf(numel(inbuf));

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [min, med, max] = getMinMaxMed_2d(inbuf)

[nrows, ncols] = size(inbuf);
max = inbuf(1, 1);
med = inbuf(ceil(nrows/2), ceil(ncols/2));
min = inbuf(nrows, ncols);

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function new_pixel  = get_new_pixel(...
    min3, med3, max3, ...
    min5, med5, max5, ...
    min7, med7, max7, ...
    min9, med9, max9, ...
    center_data)

if (med3 > min3 && med3 < max3)
    new_pixel = get_center_data(min3, med3, max3,center_data);
elseif (med5 > min5 && med5 < max5)
    new_pixel = get_center_data(min5, med5, max5,center_data);
elseif (med7 > min7 && med7 < max7)
    new_pixel = get_center_data(min7, med7, max7,center_data);
elseif (med9 > min9 && med9 < max9)
    new_pixel = get_center_data(min9, med9, max9,center_data);
else
    new_pixel = center_data;
end

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [new_data] = get_center_data(min,med,max,center_data)
if center_data <= min || center_data >= max
    new_data = med;
else
    new_data = center_data;
end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% perform median 1d computation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function outbuf = get_median_1d(inbuf)

numpixels = length(inbuf);

tbuf = inbuf;

for ii=coder.unroll(1:numpixels)
    if bitand(ii,uint32(1)) == 1  
        tbuf = compare_stage1(tbuf);
    else
        tbuf = compare_stage2(tbuf);
    end
end

outbuf = tbuf;

end

function outbuf = compare_stage1(inbuf)
numpixels = length(inbuf);
tbuf = compare_stage(inbuf(1:numpixels-1));
outbuf = [tbuf(:)' inbuf(numpixels)];
end

function outbuf = compare_stage2(inbuf)
numpixels = length(inbuf);
tbuf = compare_stage(inbuf(2:numpixels));
outbuf = [inbuf(1) tbuf(:)'];
end

function [outbuf] = compare_stage(inbuf)

step = 2;
numpixels = length(inbuf);

outbuf = inbuf;

for ii=coder.unroll(1:step:numpixels)
    t = compare_pixels([inbuf(ii), inbuf(ii+1)]);
    outbuf(ii) = t(1);
    outbuf(ii+1) = t(2);
end

end

function outbuf = compare_pixels(inbuf)
if (inbuf(1) > inbuf(2))
    outbuf = [inbuf(1), inbuf(2)];
else
    outbuf = [inbuf(2), inbuf(1)];
end
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% perform median 2d computation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function outbuf = get_median_2d(inbuf)

outbuf = inbuf;
[nrows, ncols] = size(inbuf);
for ii=coder.unroll(1:ncols)
    colData = outbuf(:, ii)';
    colDataOut = get_median_1d(colData)';
    outbuf(:, ii) = colDataOut;
end
for ii=coder.unroll(1:nrows)
    rowData = outbuf(ii, :);
    rowDataOut = get_median_1d(rowData);
    outbuf(ii, :) = rowDataOut;
end

end

The MATLAB function is modular and uses several functions to filter the noise in the image.

Adaptive Filter MATLAB Test Bench

A MATLAB test bench mlhdlc_median_filter_tb exercises the filter design by using a representative input range.

Review the MATLAB test bench:

edit(testbench_name);

I = imread('mlhdlc_img_pattern_noisy.tif');
J = I;

%   Copyright 2011-2019 The MathWorks, Inc.

smax = 9;
[nrows, ncols] = size(I);
ll = ceil(smax/2);
ul = floor(smax/2);

for ii=1:ncols-smax
    for jj=1:nrows-smax
        
        c_idx = ii;                    
        c_data = double(I(jj:jj+smax-1, ii));            
                
        [pixel_val, pixel_valid] = mlhdlc_median_filter(c_data, c_idx);
        
        if pixel_valid
            J(jj, ii) = pixel_val;
        end
    end
end

h = figure;
set( h, 'Name', [ mfilename, '_plot' ] );
subplot( 1, 2, 1 );
imshow( I, [  ] );
subplot( 1, 2, 2 );
imshow( J, [  ] );

Test the MATLAB Algorithm

To avoid run-time errors, simulate the design with the test bench.

mlhdlc_median_filter_tb

Accelerating the Design for Faster Simulation

To simulate the test bench faster:

1. Create a MEX file by using MATLAB Coder™. The HDL Workflow Advisor automates these steps when running fixed-point simulations of the design.

  codegen -o mlhdlc_median_filter -args {zeros(9,1), 0} mlhdlc_median_filter
  [~, tbn] = fileparts(testbench_name);

2. Simulate the design by using the MEX file. When you run the test bench, HDL Coder uses the MEX file and runs the simulation faster.

  mlhdlc_median_filter_tb

3. Clean up the MEX file.

  clear mex;
  rmdir('codegen', 's');
  delete(['mlhdlc_median_filter', '.', mexext]);

Create an HDL Coder Project

1. Create an HDL Coder project:

coder -hdlcoder -new mlhdlc_med_filt_prj

2. Add the file mlhdlc_median_filter.m to the project as the MATLAB Function and mlhdlc_median_filter_tb.m as the MATLAB Test Bench.

3. Click Autodefine types and use the recommended types for the inputs and outputs of the MATLAB function mlhdlc_median_filter.

Refer to Get Started with MATLAB to HDL Workflow for a more complete tutorial on creating and populating MATLAB HDL Coder projects.

Run Fixed-Point Conversion and HDL Code Generation

Click the Workflow Advisor button to start the Workflow Advisor.
Right click the HDL Code Generation task and select Run to selected task.

A single HDL file mlhdlc_median_filter_fixpt.vhd is generated for the MATLAB design. To examine the generated HDL code for the filter design, click the hyperlinks in the Code Generation Log window.

If you want to generate a HDL file for each function in your MATLAB design, in the Advanced tab of the HDL Code Generation task, select the Generate instantiable code for functions check box. See also Generate Instantiable Code for Functions.