Python image Processing (16): Image Pyramid

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http://blog.csdn.net/lights_joy/

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Image Pyramid is a kind of multi-scale expression in image, which is originally used for machine vision and image compression. The pyramid of an image is a collection of images that are progressively reduced in pyramid shape and derived from the same original image. It is obtained by echelon down sampling until a termination condition is reached to stop sampling. the bottom of the pyramid is a high-resolution representation of the image to be processed, while the top is a low-resolution approximation. The higher the level, the smaller the image, and the lower the resolution.

In general, two types of image pyramids often appear in the literature and in practical applications. They were :

Gauss Pyramid (gaussianpyramid): used to sample downward, the main image pyramid

Laplace Pyramid (laplacianpyramid): used to reconstruct the upper layer of the image from the lower pyramid images, in the digital image processing is also the prediction of residual error, the image can be maximized to restore, with the use of Gaussian pyramid.  

The brief difference between the two is that the Gaussian pyramid is used to sample the image downward, while the Laplace pyramid is used to reconstruct an image from the bottom of the pyramid image.

the up and down sampling in the image pyramid is implemented by the OpenCV function pyrup and pyrdown , respectively . here the downward and upward sampling, is the size of the image (and the direction of the pyramid opposite), upward is the image size doubled, downward is the image size halved.

pryup and prydown is not reciprocal, i.e. pryup is not a de-sampled inverse operation. In this case, the image first expands to twice times the original on each dimension, and the new row (even rows) is 0 fill. The specified filter is then convolution (actually a filter that expands to twice times the original in each dimension) to estimate the approximate value of the "lost" pixel.

with Python Try :

# image Pyramid src = cv2.imread (' f:\\tmp\\cotton.jpg ') cv2.imshow (' src ', src) # under Sample DST = Cv2.pyrdown (src) cv2.imshow (' DST ', DST) # On sample SRC1 = Cv2.pyrup (DST) cv2.imshow (' Src1 ', Src1) Cv2.waitkey ()

Our original image:

Below the sampled image:

Image after Restore:

The image is obviously blurred.

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Python image Processing (16): Image Pyramid