Color images denoising using complementary color wavelet transform

Yıl 2024, Cilt: 30 Sayı: 2, 174 - 181, 30.04.2024

Mücahit Cihan , Murat Ceylan

Öz

The RGB color ring is known as the most understandable color representation in human vision, as it has complementary colors. However, color relationships hardly ever play a function in wavelet-primarily based totally color image processing tools. In this study, Complementary Color Wavelet Transform (CCWT), which is supported by complementary color relationships and complex wavelet design techniques, is used to denoise in color images. This wavelet consists of a family of two-dimensional complex wavelets with a phase difference of 2π/3 obtained from the angle relationship between the color axes of the RGB color ring, and is very effective in terms of directional selectivity. By using the coefficients of the directions in different phases, denoising processes are performed from the multi-channel color images. It was validated the performance of CCWT using various color images and noise levels, based on peak signal-to-noise ratio, structural similarity index, mean square error values, and visual quality. CCWT was compared with state-of-the-art multi-resolution image denoising algorithms, and found that the method achieves superior denoising performance both quantitatively and visually. It was also analyzed the computation time of CCWT and compared it with existing approaches.

Anahtar Kelimeler

Complementary color wavelet transform, Color image denoising, Wavelet thresholding, Gaussian noise

Tamamlayıcı renk dalgacık dönüşümü kullanılarak renkli görüntülerin gürültüden arındırılması

Öz

RGB renk halkası, tamamlayıcı renklere sahip olduğu için insan görmesinde en anlaşılabilir renk temsili olarak bilinmektedir. Bununla birlikte, renk ilişkileri, dalgacık tabanlı renkli görüntü işleme araçlarında neredeyse hiç rol oynamamaktadır. Bu çalışmada tamamlayıcı renk ilişkileri ve kompleks dalgacık tasarım tekniklerine dayanan, Tamamlayıcı Renk Dalgacık Dönüşümü (TRDD), renkli görüntülerde gürültülerin giderilmesi için kullanılmıştır. Bu dalgacık, RGB renk halkasında bulunan renk eksenleri arasındaki açı ilişkilerinden elde edilen 2π/3 faz farklarına sahip, 2 boyutlu kompleks dalgacıklardan oluşan bir aileden meydana gelmektedir ve yönsel seçicilik bakımdan çok etkilidir. Farklı fazdaki yönlere ait katsayılar kullanılarak çok kanallı renkli görüntülerden gürültü giderme işlemleri gerçekleştirilmiştir. Farklı renkli görüntüler ve gürültü seviyeleri kullanılarak TRDD'nin performansı, tepe sinyal-gürültü oranı, yapısal benzerlik indeksi, ortalama kare hata değerleri ve görsel kaliteye dayalı olarak doğrulanmıştır. TRDD, en gelişmiş çok çözünürlüklü görüntü gürültü giderme algoritmalarıyla karşılaştırılmış ve yöntemin hem niceliksel hem de görsel olarak daha üstün gürültü giderme performansı elde ettiği görülmüştür. Ayrıca TRDD'nin hesaplama süresi analiz edilmiş ve mevcut yaklaşımlarla karşılaştırılmıştır.

Anahtar Kelimeler

Tamamlayıcı renk dalgacık dönüşümü, Renkli görüntü gürültü giderme, Dalgacık eşikleme, Gauss gürültüsü

Kaynakça

• [1] Donoho DL. “De-noising by soft-thresholding”. IEEE Transactions on Information Theory, 41(3), 613-627, 1995.
• [2] Fan L, Zhang F, Fan H, Zhang C. “Brief review of image denoising techniques”. Visual Computing for Industry, Biomedicine, and Art, 2(1), 1-12, 2019.
• [3] Zhang J, Cao L, Wang T, Fu W, Shen W. “NHNet: A non‐local hierarchical network for image denoising”. IET Image Processing, 16(9), 2446-2456, 2022.
• [4] Xu J, Zhang L, Zhang D, Feng X. “Multi-channel Weighted nuclear norm minimization for real color image denoising”. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22-29 October 2017.
• [5] Singh A, Sethi G, Kalra GS. “Spatially adaptive image denoising via enhanced noise detection method for grayscale and color images”. IEEE Access, 8, 112985-113002, 2020.
• [6] Qin N, Gong Z. “Color image denoising by means of three-dimensional discrete fuzzy numbers”. The Visual Computer, 39(5), 2051-2063, 2023.
• [7] Srisailam C, Sharma P, Suhane S. “Color image denoising using wavelet soft thresholding”. International Journal of Emerging Technology and Advanced Engineering, 4(7), 474-478, 2014.
• [8] Gai S. “Multiresolution monogenic wavelet transform combined with bivariate shrinkage functions for color image denoising”. Circuits, Systems, and Signal Processing, 37(3), 1162-1176, 2018.
• [9] Gai S, Bao Z, Zhang K. “Vector extension of quaternion wavelet transform and its application to colour image denoising”. IET Signal Processing, 13(2), 133-140, 2019.
• [10] Sifuzzaman M, Islam MR, Ali MZ. “Application of wavelet transform and its advantages compared to Fourier transform”. Journal of Physical Science, 13, 121-134, 2009.
• [11] Srisailam C, Sharma P, Suhane S. “Color image denoising using wavelet soft thresholding”. International Journal of Emerging Technology and Advanced Engineering, 4(7), 474-478, 2014.
• [12] Lim BR, Lee HS, Park RH, Yang S. “A wavelet packet-based noise reduction algorithm of NTSC images using CVBS characteristics”. IEEE Transactions on Consumer Electronics, 55(4), 2407-2415, 2009.
• [13] Liu W, Yan Q, Zhao Y. “Densely self-guided wavelet network for image denoising”. IEEE/CVF 2020 Conference on Computer Vision and Pattern Recognition Workshops, Seattle, Washington, USA, 14-19 June 2020.
• [14] Wang Y, Zhang W, Li W, Yu X, Yu N. “Non-additive cost functions for color image steganography based on inter-channel correlations and differences”. IEEE Transactions on Information Forensics and Security, 15, 2081-2095, 2019.
• [15] Chen Y, Li D, Zhang JQ. “Complementary color wavelet: A novel tool for the color image/video analysis and processing”. IEEE Transactions on Circuits and Systems for Video Technology, 29(1), 12-27, 2017.
• [16] Stokley SR. Historic Look on Color Theory. Honors Theses, Providence Campus, Johnson & Wales University, Rhode Island, USA, 2018.
• [17] Pridmore RW. “Complementary colors theory of color vision: Physiology, color mixture, color constancy and color perception”. Color Research & Application, 36(6), 394-412, 2011.
• [18] Pridmore RW. “Complementary colors: a literature review”. Color Research & Application, 46(2), 482-488, 2021.
• [19] Cihan M, Ceylan M. “Fusion of CT and MR liver ımages using multiresolution analysis methods”. Avrupa Bilim ve Teknoloji Dergisi, (30), 56-61, 2021.
• [20] Unser M, Sage D, Van De Ville D. “Multiresolution monogenic signal analysis using the Riesz–Laplace wavelet transform”. IEEE Transactions on Image Processing, 18(11), 2402-2418, 2009.
• [21] Gai S. “Multiresolution monogenic wavelet transform combined with bivariate shrinkage functions for color image denoising”. Circuits, Systems, and Signal Processing, 37, 1162-1176, 2018.
• [22] Kadri O, Baarir ZE, Schaefer G, Korovin I. “Colour ımage denoising using curvelets and scale dependent shrinkage”. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Canada, 11-14 October 2020.
• [23] Mbarki Z, Seddik H. Rapid Medical İmages Restoration Combining Parametric Wiener Filtering and Wave Atom Transform Based on Local Adaptive Shrinkage. Editors: Hanen I, Thierry V. Smart Systems for E-Health, 49-77, WBAN Technologies, Security and Applications, 2021.
• [24] Giri KJ, Quadri SMK, Bashir R, Bhat JI. “DWT based color image watermarking: a review”. Multimedia Tools and Applications, 79, 32881-32895, 2020.
• [25] Longkumer M, Gupta H. “Image denoising using wavelet transform, median filter and soft thresholding”. International Research Journal of Engineering and Technology, 5(7), 729-732, 2018.
• [26] Unser M, Sage D, Van De Ville D. “Multiresolution monogenic signal analysis using the Riesz–Laplace wavelet transform”. IEEE Transactions on Image Processing, 18(11), 2402-2418, 2009.
• [27] Van De Ville D, Blu T, Unser M. “Isotropic polyharmonic B-splines: Scaling functions and wavelets”. IEEE Transactions on Image Processing, 14(11), 1798-1813, 2005.
• [28] Donoho DL, Duncan MR. “Digital curvelet transform: strategy, implementation, and experiments”. In Wavelet Applications VII, 4056, 12-30, 2000.
• [29] Shensa MJ. “The discrete wavelet transform: wedding the a trous and Mallat algorithms”. IEEE Transactions on Signal Processing, 40(10), 2464-2482, 1992.
• [30] Fiddy MA. “The Radon transform and some of its applications”. Optica Acta: International Journal of Optics, 32(1), 3-4, 1985.
• [31] Aili W, Ye Z, Shaoliang M, Mingji Y. “Image denoising method based on curvelet transform”. In 2008 3rd IEEE Conference on Industrial Electronics and Applications, Singapore, 03-05 June 2008.
• [32] Demanet L, Ying L. “Wave atoms and sparsity of oscillatory patterns”. Applied and Computational Harmonic Analysis, 23(3), 368-387, 2007.