Histogram Segmentation Technique Using Local Maxima for Image Enhancement
Article Sidebar
Main Article Content
Abstract
Different digital images have occurred from digital cameras and different ways of taking images. Therefore, methods to improve the quality of these images have been developed. This paper has presented a method to improve a quality image which is four steps. The first step is to apply the CLAHE method to improve image quality. Next step, take the histogram images of the previous step divided into many sections that are determined by the local maxima method. In the third step, each section is adjusted by the histogram equalization. Finally step, the new histogram is converted to a new gray-level color image. The experiment results of research show that the mean absolute luminance error values of images. These values are compared with values the traditional method. It was found that this method gave better results. Although this method takes more time to process than the traditional method.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสารวิชาการปทุมวันถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรง ซึ่งกองบรรณาธิการวารสารไม่จำเป็นต้องเห็นด้วยหรือร่วมรับผิดชอบใดๆ
บทความ ข้อมูล เนื้อหา ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารวิชาการปทุมวันถือเป็นลิขสิทธิ์ของวารสารวิชาการปทุมวัน หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากวารสารวิชาการปทุมวันก่อนเท่านั้น
References
Kim, Y. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43(1), 1-8.
Gonzalez, R. C. & Woods, R. E. (2002). Digital Image Processing. New Jersey: Prentice Hall.
Chen, S. & Ramli, A. R. (2003). Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49(4), 1310-1319.
Yeganeh, A. Z. H. & Rezaie, A. (2008). A novel approach for contrast enhancement based on Histogram Equalization. In 2008 International Conference on Computer and Communication Engineering (p. 256–260). 13-15 May, 2008, Kuala Lumpur, Malaysia.
Parveen, N. R. S. & Sathik, M. M. (2009). Enhancement of Bone Fracture Images by Equalization Methods. In 2009 International Conference on Computer Technology and Development (Vol. 2, p. 391–394). 13-15 November, 2009, Kota Kinabalu, Malaysia.
Chang, Y. & Chang, C. (2010). A simple histogram modification scheme for contrast enhancement. IEEE Transactions on Consumer Electronics, 56(2), 737–742.
Li, W. (2011). A new algorithm of histogram equalization in infrared image. In 2011 International Conference on Electrical and Control Engineering (p. 957–958). 16-18 September, 2011, Yichang, China.
Fazli, S. et al. (2013). A novel retinal vessel segmentation based on local adaptive histogram equalization. In 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP) (p. 131–135). 10-12 September, 2013, Zanjan, Iran.
Zuiderveld, K. (1994). Contrast Limited Adaptive Histogram Equalization. In Hackbert, P. S. (Ed.). Graphics Gems IV (p. 474-485). San Diego, California: Academic Press.
Wayalun, P. et al. (2013). Evaluation of Automated Chromosome G-band Images Enhancement Using Histogram Equalization. KKU Research Journal, 18(2), 240–256. (in Thai)
Wisassakwichai, C. et al. (2019). Computational method for color vision of coffee bean roastingusing openCV. UTK RESEARCH JOURNAL, 13(2), 25-38. (in Thai)
Kingboo, A. & Chariyathitipong, P. (2020). Interpolation-Based Enhancement Technique for Image Enlargement. Journal of Science and Technology, Ubon Ratchathani University, 22(1), 148-156. (in Thai)
Nakai, K. et al. (2013). Color image contrast enhacement method based on differential intensity/saturation gray-levels histograms. In 2013 International Symposium on Intelligent Signal Processing and Communication Systems (p.445-449). 12-15 November, 2013, Naha, Japan.
Nakajima, N. & Taguchi, A. (2014). Intensity/saturation enhancement based on differential gray-levels histogram equalization. In 2014 IEEE Asia Pacific Conference on Circuits and Systems (p.527-530). 17-20 November, 2014, Ishigaki, Japan.
Taguchi, A. (2017). Color Systems and Color Image Enhancement Methods. ECTI Transactions on Computer and Information Technology, 10(2), 97–110.
Wiangsamut, S. et al. (2006). Fingerprint Image Enhancement Using Voting Algorithm. Asia-Pacific Journal of Science and Technology, 11(3), 213–227. (in Thai)
Giulio, S. et al. (2020). Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE). Sustainability, 12(20), 8573-8589.
Lee, H. et al. (2023). Chest Radiographs Enhancement with Contrast Limited Adaptive Histogram. In 2023 17th International Conference on Ubiquitous Information Management and Communication (p.1-2). 3-5 January, 2023, Seoul, Republic of Korea.
Belaid, A., et al. (2011). Implicit active contours for ultrasound images segmentation driven by phase information and local maximum likelihood. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. (p.630–635). 30 March - 2 April, 2011, Chicago, IL, USA.
Nafkha, A. (2013). Near maximum likelihood detection algorithm based on 1-flip local search over uniformly distributed codes. In 2013 IEEE International Conference on Communications (p.4900–4904). 9-13 June, 2013, Budapest, Hungary.
Saravanan, C. (2010). Color Image to Grayscale Image Conversion. In 2010 Second International Conference on Computer Engineering and Applications (vol. 2, p.196–199). 19-21 march, 2010, Bali, Indonesia.