Improving Image Quality Based on Feature Extraction and Gaussian Model

By expanding the use of digital images in various areas of everyday life, such as medicine, identification, satellite imagery, and even personal cameras and machine vision, it is felt more effective in applying quality improvements to the images used. The low-quality images in the machine's vision can expose the efficacy of later processing, such as feature extraction, classification, and pattern recognition. In this thesis, a new method for improving the quality of images based on the extraction of Godin’s combined feature and model has been proposed. Based on the fact that each homogeneous region in the image has a Gaussian distribution histogram, this distribution can be divided into smaller histograms. For the histogram division efficacy, the image is transmitted from the RGB space to the HSV space and the histogram division is applied to the severity region, and the histogram is applied to each sub Histogram based on the statistical characteristics, and the image. Improved results are returned to the RGB color space. Several qualitative and quantitative criteria have been used to evaluate the proposed method. Qualitative comparison results show improved image quality compared to histogram equivalence methods and linear contrast traction. Quantitative evaluation criteria, such as entropy and spatial frequency, as well as signal to noise ratio, and peak signal to noise ratio, are generally proposed for superiority of the method.