Block Processing Large Images

Open this Example

This example shows how to use blockproc to perform edge detection on a TIFF image. When working with large images, normal image processing techniques can sometimes break down. The images can either be too large to load into memory, or else they can be loaded into memory but then be too large to process.

To avoid these problems, you can process large images incrementally: reading, processing, and finally writing the results back to disk, one region at a time. The blockproc function helps you with this process. Using blockproc, specify an image, a block size, and a function handle. blockproc then divides the input image into blocks of the specified size, processes them using the function handle one block at a time, and then assembles the results into an output image. blockproc returns the output to memory or to a new file on disk.

See the documentation for blockproc for detailed syntax information.

First, consider the results of performing edge detection without block processing. This example uses a small image, cameraman.tif, to illustrate the concepts, but block processing is often more useful for large images.

file_name = 'cameraman.tif';

I = imread(file_name);

normal_edges = edge(I,'canny');

imshow(I);

title('Original Image');

figure

imshow(normal_edges);

title('Conventional Edge Detection');

Now try the same task using block processing. The blockproc function has built-in support for TIFF images, so you do not have to read the file completely into memory using imread. Instead, call the function using the string filename as input. blockproc reads in one block at a time, making this workflow ideal for very large images.

When working with large images you will often use the 'Destination' parameter to specify a file into which blockproc will write the output image. However, in this example you will return the results to a variable, in memory.

This example uses a block size of [50 50]. In general, choosing larger block sizes yields better performance for blockproc. This is particularly true for file-to-file

workflows where accessing the disk will incur a significant performance cost. Appropriate block sizes vary based on the machine resources available, but should likely be in the range of thousands of pixels per dimension.

% You can use an anonymous function to define the function handle. The

% function is passed a structure as input, a "block struct", with

several

% fields containing the block data as well as other relevant

information.

% The function should return the processed block data.

edgeFun = @(block_struct) edge(block_struct.data,'canny');

block_size = [50 50];

block_edges = blockproc(file_name,block_size,edgeFun);

figure

imshow(block_edges);

title('Block Processing - Simplest Syntax');

Notice the significant artifacts from the block processing. Determining whether a pixel is an edge pixel or not requires information from the neighboring pixels. This means that each block cannot be processed completely separately from its surrounding pixels. To remedy this, use the blockproc parameter 'BorderSize' to specify vertical and horizontal borders around each block. The necessary 'BorderSize' varies depending on the task being performed.

border_size = [10 10];

block_edges =

blockproc(file_name,block_size,edgeFun,'BorderSize',border_size);

figure

imshow(block_edges);

title('Block Processing - Block Borders');

The blocks are now being processed with an additional 10 pixels of image data on each side. This looks better, but the result is still significantly different from the original in-memory result. The reason for this is that the Canny edge detector uses a threshold that is computed based on the complete image histogram. Since the blockproc function calls the edge function for each block, the Canny algorithm is working with incomplete histograms and therefore using varying thresholds across the image.

When block processing images, it is important to understand these types of algorithm constraints. Some functions will not directly translate to block processing for all syntaxes. In this case, the edge function allows you to pass in a fixed threshold as an input argument instead of computing it. Modify your function handle to use the three-argument syntax of edge, and thus remove one of the "global" constraints of the function. Some trial and error finds that a threshold of 0.09 gives good results.

thresh = 0.09;

edgeFun = @(block_struct) edge(block_struct.data,'canny',thresh);

block_edges =

blockproc(file_name,block_size,edgeFun,'BorderSize',border_size);

figure

imshow(block_edges);

title('Block Processing - Borders & Fixed Threshold');

The result now closely matches the original in-memory result. You can see some additional artifacts along the boundaries. These are due to the different methods of padding used by the Canny edge detector. Currently, blockproc only supports zero-padding along the image boundaries.

Computing Statistics for Large Images

Open this Example

This example shows how to use blockproc to compute statistics from large images and then use that information to more accurately process the images blockwise. The blockproc function is well suited for applying an operation to an image blockwise, assembling the results, and returning them as a new image. Many image processing algorithms, however, require "global" information about the image, which is not available when you are only considering one block of image data at a time. These constraints can prove to be problematic when working with images that are too large to load completely into memory.

This example performs a task similar to that found in the Enhancing Multispectral Color Composite Images example, but adapted for large images using blockproc. You will be enhancing the visible bands of the Erdas LAN file rio.lan. These types of block processing techniques are typically more useful for large images, but a small image will work for the purpose of this example.

Step 1: Construct a Truecolor Composite

Using blockproc, read the data from rio.lan, a file containing Landsat thematic mapper imagery in the Erdas LAN file format. blockproc has built-in support for reading TIFF and JPEG2000 files only. To read other types of files you must write an Image Adapter class to support I/O for your particular file format. This example uses a pre-built Image Adapter class, the LanAdapter, which supports reading LAN files. For more information on writing Image Adapter classes see the tutorial in the Users' Guide describing how the LanAdapter class was built.

The Erdas LAN format contains the visible red, green, and blue spectrum in bands 3, 2, and 1, respectively. Use blockproc to extract the visible bands into an RGB image.

% Create the LanAdapter object associated with rio.lan.

input_adapter = LanAdapter('rio.lan');

% Select the visible R, G, and B bands.

input_adapter.SelectedBands = [3 2 1];

% Create a block function to simply return the block data unchanged.

identityFcn = @(block_struct) block_struct.data;

% Create the initial truecolor image.

truecolor = blockproc(input_adapter,[100 100],identityFcn);

% Display the un-enhanced results.

figure;

imshow(truecolor);

title('Truecolor Composite (Un-enhanced)');

The resulting truecolor image is similar to that of paris.lan in the Enhancing Multispectral Color Composite Images example. The RGB image appears dull, with little contrast.

Step 2: Enhance the Image - First Attempt

First, try to stretch the data across the dynamic range using blockproc. This first attempt simply defines a new function handle that calls stretchlim and imadjust on each block of data individually.

adjustFcn = @(block_struct) imadjust(block_struct.data,...

stretchlim(block_struct.data));

truecolor_enhanced = blockproc(input_adapter,[100 100],adjustFcn);

figure

imshow(truecolor_enhanced)

title('Truecolor Composite with Blockwise Contrast Stretch')

You can see immediately that the results are incorrect. The problem is that the stretchlim function computes the histogram on the input image and uses this information to compute the stretch limits. Since each block is adjusted in isolation from its neighbors, each block is computing different limits from its local histogram.

Step 3: Examine the Histogram Accumulator Class

To examine the distribution of data across the dynamic range of the image, you can compute the histogram for each of the three visible bands.

When working with sufficiently large images, you cannot simply call imhist to create an image histogram. One way to incrementally build the histogram is to use blockproc with a class that will sum the histograms of each block as you move over the image.

Examine the HistogramAccumulator class.

type HistogramAccumulator

% HistogramAccumulator Accumulate incremental histogram.

% HistogramAccumulator is a class that incrementally builds up a

% histogram for an image. This class is appropriate for use with 8-

bit

% or 16-bit integer images and is for educational purposes ONLY.

% Copyright 2009 The MathWorks, Inc.

classdef HistogramAccumulator < handle

properties

Histogram

Range

end

methods

function obj = HistogramAccumulator()

obj.Range = [];

obj.Histogram = [];

end

function addToHistogram(obj,new_data)

if isempty(obj.Histogram)

obj.Range = double(0:intmax(class(new_data)));

obj.Histogram = hist(double(new_data(:)),obj.Range);

else

new_hist = hist(double(new_data(:)),obj.Range);

obj.Histogram = obj.Histogram + new_hist;

end

end

end

end

The class is a simple wrapper around the hist function, allowing you to add data to a histogram incrementally. It is not specific to blockproc. Observe the following simple use of the HistogramAccumulator class.

% Create the HistogramAccumulator object.

hist_obj = HistogramAccumulator();

% Split a sample image into 2 halves.

full_image = imread('liftingbody.png');

top_half = full_image(1:256,:);

bottom_half = full_image(257:end,:);

% Compute the histogram incrementally.

addToHistogram(hist_obj, top_half);

addToHistogram(hist_obj, bottom_half);

computed_histogram = hist_obj.Histogram;

% Compare against the results of IMHIST.

normal_histogram = imhist(full_image);

% Examine the results. The histograms are numerically identical.

figure

subplot(1,2,1);

stem(computed_histogram,'Marker','none');

title('Incrementally Computed Histogram');

subplot(1,2,2);

stem(normal_histogram','Marker','none');

title('IMHIST Histogram');

Step 4: Use the HistogramAccumulator Class with BLOCKPROC

Now use the HistogramAccumulator class with blockproc to build the histogram of the red band of data in rio.lan. You can define a function handle for blockproc that will invoke the addToHistogram method on each block of data. By viewing this histogram, you can see that the data is concentrated within a small part of the available dynamic range. The other visible bands have similar distributions. This is one reason why the original truecolor composite appears dull.

% Create the HistogramAccumulator object.

hist_obj = HistogramAccumulator();

% Setup blockproc function handle

addToHistFcn = @(block_struct) addToHistogram(hist_obj,

block_struct.data);

% Compute histogram of the red channel. Notice that the addToHistFcn

% function handle does generate any output. Since the function handle

we

% are passing to blockproc does not return anything, blockproc will

not

% return anything either.

input_adapter.SelectedBands = 3;

blockproc(input_adapter,[100 100],addToHistFcn);

red_hist = hist_obj.Histogram;

% Display results.

figure

stem(red_hist,'Marker','none');

title('Histogram of Red Band (Band 3)');

Step 5: Enhance the Truecolor Composite with a Contrast Stretch

You can now perform a proper contrast stretch on the image. For conventional, in-memory workflows, you can simply use the stretchlim function to compute the arguments to imadjust (like the MultispectralImageEnhancementExample example does). When working with large images, as we have seen, stretchlim is not easily adapted for use with blockproc since it relies on the full image histogram.

Once you have computed the image histograms for each of the visible bands, compute the proper arguments to imadjust by hand (similar to how stretchlim does).

First compute the histograms for the green and blue bands.

% Compute histogram for green channel.

hist_obj = HistogramAccumulator();

addToHistFcn = @(block_struct) addToHistogram(hist_obj,

block_struct.data);

input_adapter.SelectedBands = 2;

blockproc(input_adapter,[100 100],addToHistFcn);

green_hist = hist_obj.Histogram;

% Compute histogram for blue channel.

hist_obj = HistogramAccumulator();

addToHistFcn = @(block_struct) addToHistogram(hist_obj,

block_struct.data);

input_adapter.SelectedBands = 1;

blockproc(input_adapter,[100 100],addToHistFcn);

blue_hist = hist_obj.Histogram;

Now compute the CDF of each histogram and prepare to call imadjust.

computeCDF = @(histogram) cumsum(histogram) / sum(histogram);

findLowerLimit = @(cdf) find(cdf > 0.01, 1, 'first');

findUpperLimit = @(cdf) find(cdf >= 0.99, 1, 'first');

red_cdf = computeCDF(red_hist);

red_limits(1) = findLowerLimit(red_cdf);

red_limits(2) = findUpperLimit(red_cdf);

green_cdf = computeCDF(green_hist);

green_limits(1) = findLowerLimit(green_cdf);

green_limits(2) = findUpperLimit(green_cdf);

blue_cdf = computeCDF(blue_hist);

blue_limits(1) = findLowerLimit(blue_cdf);

blue_limits(2) = findUpperLimit(blue_cdf);

% Prepare argument for IMADJUST.

rgb_limits = [red_limits' green_limits' blue_limits'];

% Scale to [0,1] range.

rgb_limits = (rgb_limits - 1) / (255);

Create a new adjustFcn that applies the global stretch limits and use blockproc to adjust the truecolor image.

adjustFcn = @(block_struct) imadjust(block_struct.data,rgb_limits);

% Select full RGB data.

input_adapter.SelectedBands = [3 2 1];

truecolor_enhanced = blockproc(input_adapter,[100 100],adjustFcn);

figure;

imshow(truecolor_enhanced)

title('Truecolor Composite with Corrected Contrast Stretch')

The resulting image is much improved, with the data covering more of the dynamic range, and by using blockproc you avoid loading the whole image into memory.