RECURSPIVE SEPARABLE 2D DIGITAL FILTER FOR INCREASING THE SHARPNESS OF RGB IMAGES

Abstract

An important role in the perception of image quality is sharpness, that is, the An important role in the perception of image quality is played by sharpness, that is, the magnitude of the brightness gradient in areas near the boundaries of objects. This characteristic is responsible for the clarity and detail of small image elements. Defocusing the camera lens and insufficient illumination are the main factors that can lead to digital image blurring. To increase the sharpness, various processing methods are used, such as filtering in the frequency domain, for example, the use of fast Fourier transform to emphasize the boundaries and textures of the image. The use of this typeof filtering allows you to control the contrast and frequency content of the image, which leads to an improvement in visual perception. However, this method has a number of significant drawbacks, such as logarithmic complexity and performing additional calculations associated with forward and inverse Fourier transforms. Therefore, the preferred method of image sharpening is the so-called spatial processing, which provides direct filtering of image pixels without additional transformations, and the reuse of processing results (recursive component) in the filter allows you to reduce the number of operations, reduce computational complexity. The article describes the development of an effective recursive separable two-dimensional digital filter to sharpen largedimensional RGB images. The algorithms of its construction are given, the corresponding block diagrams are designed. The filter has the property of more uniform detail of image objects, and is less susceptible to the creation of pulse noise. Also, for the original high-resolution RGB image, a blur filter is modeled, the matrix of which is filled according to the normal (Gaussian) law. To assess the filtration quality, the developed filter is compared with the algorithm of classical two-dimensional convolution with a 5x5 Laplace high-pass filter core.