A new method proposal to enhance foreground images against noisy backgrounds: Haytham Thresholding

Yıl 2024, , 12 - 19, 29.12.2024

Esin Mutlu Serkan Dereli

https://doi.org/10.18466/cbayarfbe.1485592

Öz

Since only pixel intensities are taken into account in the binarization of gray images during the thresholding stage, it brings with it a significant problem. Because, since the relationship between pixels in the image is neglected, it is seen that noises are sometimes defined as an object, sometimes plays a role in changing the detected object, especially in noisy images where illumination is not uniform. In this study, a locally adaptive thresholding algorithm called Haytham Thresholding is proposed in order to eliminate these limitations of global thresholding algorithms and to eliminate noise caused by lighting during the binarization of the image. Especially in the literature, it is seen that noise is high in methods performed by taking the standard deviation into account when the image has a gradient feature. To prevent this, pixel values were normalized by taking into account the weights of the pixels in the window region instead of their standard deviation. These normalized values were added to the matrix values obtained by the average filter and then subtracted from the original image matrix. In the experiments, the proposed method was compared with Otsu and three different local thresholding algorithms by using four different image types also used in the literature. The comparison of the methods was made both visually and with image quality metrics such as PSNR and SSIM. As a result, it has been observed that the proposed method produces successful results compared to both global thresholding and local thresholding algorithms frequently used in the literature.

Anahtar Kelimeler

Binarization, Image Segmentation, Image Enhancement, Image Processing, Local Tresholding

Kaynakça

• [1]. Aslam, Y, Santhi, N. 2020. A comprehensive survey on optimization techniques in image processing. Materials Today: Proceedings, vol. 24:1758-1765.
• [2]. Dargan, S, Kumar, M, Ayyagari, M, Kumar, G. 2020. A survey of deep learning and its applications: a new paradigm to machine learning., Archives of Computational Methods in Engineering, vol. 27(4):1071-1092.
• [3]. Viejo, C, G, Torrico, D, Dunshea, F, Fuentes, S. 2019. Emerging technologies based on artificial intelligence to assess the quality and consumer preference of beverages, Beverages, 5(62):1-25.
• [4]. Hamuda, E, Glavin, M, Jones, E. 2016. A survey of image processing techniques for plant extraction and segmentation in the field, Computers and Electronics in Agriculture, 125:184-199.
• [5]. Wiley, V, Lucas, T. 2018. Computer vision and image processing: a paper review, International Journal of Artificial Intelligence Research, 2:29-36.
• [6]. Dereli, S. 2020. True-Random Number Generator Based on Image Histogram, Academic Perspective Procedia, 3(1):301-307.
• [7]. Chaubey, A. 2016. Comparison of the local and global thresholding methods in image segmentation, World Journal of Research and Review, 2:1-4.
• [8]. Aqeel, E. 2015. The Use of Threshold Technique in image segmentation, Journal of the College of Basic Education, 21:797-806.
• [9]. Xiong, W., Zhou, L., Yue, L., Li, L., Wang, S. 2021. An enhanced binarization framework for degraded historical document images. EURASIP Journal on Image and Video Processing, 2021(1): 13.
• [10]. Guruprasad, P. Overview of different thresholding methods in image processing, in 3rd National Conference on ETACC, 2020.
• [11]. Kaur, D, Kaur, Y. 2014. Various image segmentation techniques: a review, International Journal of Computer Science and Mobile Computing, 3(5):809-814.
• [12]. Kang, S., Iwana, B. K., Uchida, S. 2021. Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules. Pattern Recognition, 109: 107577.
• [13]. Li, Y, Zhu, R, Mi, L, Cao, Y, Yao, D. 2016. Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method, Computational and mathematical methods in medicine, 2016:1-12.
• [14]. Khairnar, S., Thepade, S. D., Gite, S. 2021. Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade's SBTC. Intelligent Systems with Applications, 10: 200046.
• [15]. Liu, L, Yang, N, Lan, Y, Li, J. 2015. Image segmentation based on gray stretch and threshold algorithm, Optik, 126:626-629.
• [16]. Xiang, F, Jian, Z, Wei, W, Licheng, H. 2015. A new local threshold segmentation algorithm, Computer Applications and Software, 32:195-197.
• [17]. Yang, P, Song, W, Zhao, X, Zheng, R, Qingge, L. 2020. An improved Otsu threshold segmentation algorithm, International Journal of Computational Science and Engineering, 22:146-153.
• [18]. Mehta, N., Braun, P. X., Gendelman, I., Alibhai, A. Y., Arya, M., Duker, J. S., Waheed, N. K. 2020. Repeatability of binarization thresholding methods for optical coherence tomography angiography image quantification. Scientific Reports, 10(1): 15368.
• [19]. Mustafa, W, Yazid, H. 2016. Background correction using average filtering and gradient based thresholding, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8:81-88.
• [20]. Guo, Y, Y. 2014. A novel image thresholding algorithm based on neutrosophic similarity score, Measurement, 58:175-186.
• [21]. Su, B, Lu, S, Tan, C. 2012. Robust document image binarization technique for degraded document images, EEE transactions on image processing, 22:1408-1417.
• [22]. Lin, M., Ji, R., Xu, Z., Zhang, B., Chao, F., Lin, C. W., Shao, L. 2022. Siman: Sign-to-magnitude network binarization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5): 6277-6288.
• [23]. Wunnava, A, Naik, M, Panda, R, Jena, B, Abraham, A. 2020. A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer, Engineering Applications of Artificial Intelligence, 94:1-19.
• [24]. Saputra, M, Santosa, P. Obstacle Avoidance for Visually Impaired Using Auto-Adaptive Thresholding on Kinect's Depth Image, in IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops, 2014.
• [25]. Kandemir, C, Kalyoncu, C, Toygar, Ö. 2015. A weighted mean filter with spatial-bias elimination for impulse noise removal, Digital Signal Processing, 46:164-174.