Image restoration evaluation method using spatial and frequency filters

Authors

DOI:

https://doi.org/10.46299/j.isjea.20220104.02

Keywords:

Image restoration, Gaussian noise, Wiener filter, geometric mean filter, midpoint filter, Python

Abstract

When processing and transmitting information in the form of images, the problem of reducing their distortion due to various noises is important. Noise reduces the quality of the image and thus the perception of the information contained in it. Because of these problems, the ability to evaluate the information that can be obtained as a result of analysis using both visual and computer methods is impaired. The process of reducing noise in images is handled by an area of image processing called restoration. Despite the intersection of this area with image enhancement, it should be noted that the latter is a largely subjective procedure, while the restoration process is largely objective in nature. During restoration, an attempt is made to reconstruct or reproduce the distorted image, using a priori information about the phenomenon that caused its deterioration. Restoration methods are based on the modeling of distortion processes and the use of reverse procedures to restore the original image. In this paper, a new method for evaluating the restoration of distorted images, implemented with the help of spatial and frequency filters, is proposed. It consists in converting distorted images before filtering into gray scale and obtaining their histograms. The obtained histogram is extrapolated by a Gaussian curve and the value of the root mean square deviation is determined. A similar procedure is carried out for the restored image. The analysis was performed in the Python programming language using the Pillow and OpenCV image libraries. . The restoration evaluation parameter R is proposed, which is the ratio of the difference between the root mean square deviations of the distorted and restored images to the root mean square deviation of the distorted image. The R parameter was estimated for the geometric mean filter, the midpoint filter, and the Wiener filter and was found to be equal to 0.2; 0.3 and 0.43, respectively, which correlates with visual observations.

References

Ebrahimia M.A., Khoshtaghazaa M.H., Minaeia S., Jamshidi B. (2017). Vision-based pest detection based on SVM classification method. Journal of Computers and Electronics in Agriculture, 137, 52-58.

Nakonechny A., Nakonechny R., Pavlish V. (2010). Digital Image Processing. Lviv Polytechnic Publishing House. P.366

Bondarev G., Trester G., Tchernega B. (1999). Digital Image Processing. SevGTU Publishing House. P.398

Duda, H., Hart, P. E., & David, G. (2001). Stork, pattern classification. ed: John Wiley & Sons, 25, 1150-1157.

Polovynko I., Kashuba A. (2019). Metod of space image improvement by using spatial optical mask and frequency filters. Collected scientific papers Electronics and information technologies, 12, 55-63.

Половинко І.І., Кашуба А.І. (2020). Колірні перетворення космознімків із врахуванням відбитого та розсіяного світла. Міжнародний науково-технічний журнал, 65 (1), 11-16.

Polovynko Ihor, Kniazevich Lubomyr. (2021). Improvement of images by using graduate transformation of their Furrier descriptions. Technology Audit and Production Reserves, 58 (2), 16-19.

Половинко І.І., Семочко О.Г. (2022). Розпізнання образів головного мозку людини. Матеріали ХІ міжнародної конференції «Релаксаційно, нелінійно акустооптичні процеси і матеріали», 129-130.

Pixabay. Available at: https://pixabay.com/

Marion A. (1991). An Introduction to Image Processing, Chapman and Hall, 274.

Boyle, R., & Thomas, R. (1988). Computer vision: A first course. Blackwell Scientific Publications, 32 – 38

Кайлат Томас, Сайед Али Х., Хассиби Бабак. (2000). Линейная оценка. Прентис-Холл, Нью-Джерси.

Винер Н. (1942). Интерполяция, экстраполяция и сглаживание стационарных временных рядов. Исследовательский проект DIC-6037 MIT.

Кокошкин, А. В., Коротков, В. А., Коротков, К. В., & Новичихин, Е. П. (2015). Использование универсального опорного спектра для оценки отношения шум–сигнал в фильтре Винера. Журнал радиоэлектроники, 7, 1-17.

Penney, B. C., Glick, S. J., & King, M. A. (1990). Relative importance of the error sources in Wiener restoration of scintigrams. IEEE transactions on medical imaging, 9(1), 60-70.

Published

2022-10-01

How to Cite

Polovynko І., & Semochko О. (2022). Image restoration evaluation method using spatial and frequency filters. International Science Journal of Engineering & Agriculture, 1(4), 8–18. https://doi.org/10.46299/j.isjea.20220104.02