Local Adaptive Image Processing

Abstract

Three methods for two-dimensional local adaptive image processing are presented in this chapter. In the first one, the adaptation is based on the local information from the four neighborhood pixels of the processed image and the interpolation type is changed to zero or bilinear. An analysis of local characteristics of images in small areas is presented from which the optimal selection of thresholds for dividing into homogeneous and contour blocks is made and the interpolation type is changed adaptively. In the second one, the adaptive image halftoning is based on the generalized two-dimensional LMS error-diffusion filter for image quantization. The thresholds for comparing of input image levels are calculated from the gray values dividing the normalized histogram of the input halftone image into equal parts. The third one—the adaptive line prediction is based on two-dimensional LMS adaptation of coefficients of the linear prediction filter for image coding. An analysis of properties of 2D LMS filters in different directions was made. As a result of the performed mathematical description in the presented methods, three algorithms for local adaptive image processing was developed. The principal block schemes of the developed algorithms are presented. An evaluation of the quality of the processed images was made on the base of the calculated PSNR, SNR, MSE and the subjective observation. The given experimental results from the simulation in MATLAB environment for each of the developed algorithms, suggest that the effective use of local information contributes to minimize the processing error. The methods are extremely suitable for different types of images (for example: fingerprints, contour images, cartoons, medical signals, etc.). The developed algorithms have low computational complexity and are suitable for real-time applications.