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14 November 1996 Edge-orientation-based noise reduction with polynomial transforms
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We present a technique for directional-sensitive image restoration based on the polynomial transform. The polynomial transform is an image description model which incorporates important properties of visual perception, such as the Gaussian-derivative model of early vision. The polynomial transform basically consists of a local description of an image. Localization is achieved by multiplying the image with overlapping window functions. In the case of the discrete polynomial transform, the contents of the image within every position of the analysis window is represented by a finite set of coefficients. These coefficients correspond to the weights in a polynomial expansion that reconstructs the image within the window function. It has been showed how the polynomial transform can be used to design efficient noise-reduction algorithms by adaptively transforming the coefficients of every window according to the image contents. Other types of transformations on the polynomial coefficients lead to different image-restoration applications, such as deblurring, coding, and interpolation. In all cases, the restored image is obtained by means of an inverse polynomial transform which consists of interpolating the transformed coefficients with pattern functions that are products of a polynomial and a window function. We show in this paper how image restoration, namely noise reduction and deblurring, based on the polynomial transform can be improved by detecting the position and orientation of relevant edges in the image.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Boris Escalante-Ramirez and Juan Roman Lopez-Miranda "Edge-orientation-based noise reduction with polynomial transforms", Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996);


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