MULTISCALE DIFFERENTIAL METHOD FOR DIGITAL IMAGE SHARPENING

Abstract

We have proposed and tested a novel method for digital image sharpening. The method is based on multi-scale image analysis, calculation of differential responses of image brightness in different spatial scales, and the subsequent calculation of a restoration function, which sharpens the image by simple subtraction of its brightness values from those of the original image. The method features spatial transposition of the restoration function elements, its normalization, and taking into account the sign of the brightness differential response gradient close to the object edges. The calculation algorithm for the proposed method makes use of integer arithmetic that significantly reduces the computation time. The paper shows that for the images containing small amount of the blur due to the residual aberrations of an imaging system, only the first two scales are needed for the calculation of the restoration function. Similar to the blind deconvolution, the method requires no a priori information about the nature and magnitude of the blur kernel, but it is computationally inexpensive and is much easier in practical implementation. The most promising applications of the method are machine vision and surveillance systems based on real-time intelligent pattern recognition and decision making.

Keywords

• digital image
• image contrast
• sharpness
• blur kernel
• deconvolution