———————————Quadtree Structured Image Approximation————————————
                        (Version 1.01)

Version 1.01 of this code just provides the executable to reproduce the denoising results. 
The interpolation executable will be available in a future version, which will be released shortly.



———————————————————————Denoising syntax———————————————————————

Place the file `denoise.mexmaci64’ into your current MATLAB directory and execute the following command:

[restored,regions]=denoise(y,x,model,shifts,sigma);

Outputs:
	restored- denoised image
	regions- provides information to allow the image partitioning to be plotted

Inputs:
	y- is the degraded image
	x- is the noise free image (replace with zeros(size(y)) if unknown)
	model- is either `prune’ or `prune-join’ (it is strongly recommended to use `prune’. `prune-join’ can be very slow!
	shifts- is the number of shifts in each direction; i.e., the total number of shifts performed is shifts^2.
	sigma- is the noise standard deviation.
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———————————————————————Denoising example——————————————————————
x =double(imread('Cameraman256.png’));
randn(‘seed',0); z1= randn(size(x));
sigma=25; z=sigma*z1;
y=x+z;
[restored,regions]=denoise(y,x,'prune',16,sigma);
draw(restored);
draw_regions(regions,size(x),'r',1);
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