THE USE OF PARTIALLY LINEAR REGRESSION MODEL IN IMAGE PROCESSING

Abstract

Digital imaging systems are increasingly popular in various fields such as education, industry, engineering, and healthcare. The ease of use and low cost of these systems contribute to their widespread adoption. However, the main disadvantage of digital imaging is resolution issues. In practical applications that require high resolution, dense sensors are used to obtain robust images. This method, however, increases costs and produces more data and noise due to its density. Additionally, millions of low-resolution but valuable pieces of information are lost. Image processing techniques are used to enhance resolution and preserve high-frequency information. The primary aim of this study is to comprehensively investigate the importance and effectiveness of using partially linear models in image processing applications. Partially linear regression aims to offer a new model for image enhancement without losing high-frequency information. Because many problems encountered in the field of image processing stem from resolution issue, this study aims to understand the effects of resolution on image processing processes and to demonstrate how partially linear models can be used to address these effects. Various comparison methods have been used to evaluate the effectiveness of the proposed method. These methods have been employed to objectively assess the quality difference between images, highlighting the superiority of the proposed method over traditional methods. The study's findings show that partially linear models are a significant tool in image processing applications. Future studies may aim to examine in more detail how these models perform with different types of images and conditions.

Keywords

• Digital Imaging Systems
• Partially Linear Regression
• Image Processing
• Resolution
• Image Enhancement

References

1. Takeda, H., Farsiu, S., Milanfar, P., “Kernel Regression for Image Processing and Reconstruction”, IEEE Transactions on Image Processing, 16(2), 349-366, 2007.
2. Montgomery, D.C., Peck, E.A., Vining, G.G., Introduction to Linear Regression Analysis. 5th ed., John Wiley & Sons, New York, 2012.
3. Ni, K., Nguyen, T., “Image Superresolution Using Support Vector Regression”, IEEE Transactions on Image Processing, 16, 1596-1610, 2007.
4. Tomasi, C., Manduchi, R., “Bilateral filtering for gray and color images”, Proceeding of IEEE Int. Conf. Computer Vision, New Delhi, India, 1998, pp. 836-846.
5. Toprak, S., Partially Linear Regression Models with Measurement Errors, PhD thesis, Dicle University, Diyarbakır, Turkey, 2015.
6. Yalaz, S., Tez, M., “Partially linear multivariate regression in the presence of measurement error”, Communications for Statistical Applications and Methods, 27, 511-521, 2020.
7. Ataş, M., “Open Cezeri Library: A novel java based matrix and computer vision framework”, Computer Applications in Engineering Education, 24(5), 736-743, 2016.