———————————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.mexw64’ 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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