International Journal of Emerging Research in Engineering, Science, and Management
Vol. 1, Issue 1, pp. 18-23, Jan-Mar 2022.
https://doi.org/10.58482/ijeresm.v1i1.4
Layer Decomposition based Local Tone Mapping Operator
Zonnavada Chaithanya
G Sreenivasulu
Assistant Professor, Dept. of ECE, Geethanjali Institute of Science and Technology, Nellore
Professor, Dept. of ECE, Sri Venkateswara University, Tirupati
Abstract:
Dynamic range of an image directly defines the number of levels it can accommodate in an image. In general, a dynamic range of 256 is in use. Defining dynamic range of an image need to be done by considering the display unit on which the image will be displayed on. If the dynamic range extends over 256, the dynamic range is said to be high dynamic range. This range may extend up to 10,000. This kind of images can’t be displayed on display units which can’t differentiate that many pictorial information. This kind of pictorial information need to be converted so that the regular display units can adapt the format. This must be done without losing much information. This process is very crucial and is done by several tone mapping operators. Tone mapping operators are of two types, global and local. Global tone mapping operators apply same mapping function throughout the image while the local tone mapping operators use different mapping functions for local regions of the image. Though the quality of global TMOs is better, there are many halo effects in the converted image. In this paper, a global TMO based on decomposition is proposed intended to reduce these effects. Image is decomposed in to two layers, base, and detail. A hybrid decomposition and optimization are proposed to improve the quality of converted image.
Keywords: base layer, detail layer, retinex decomposition, TMQI, Tone mapping
References:
- Reinhard and K. Devlin. Dynamic range reduction inspired by photoreceptor physiology. IEEE Transactions on Visualization and Computer Graphics, 11(1):13–24, Jan. 2005.
- Tumblin and H. Rushmeier. Tone reproduction for realistic images. IEEE Computer Graphics and Applications, 13(6):42–48, Nov. 1993.
- Ward. A contrast-based scale factor for luminance display Graphics gems IV, pages 415–421, 1994.
- Zhang and S.Kamata, “An Adaptive Tone Mapping Algorithm for High Dynamic Range Images,” Lecture Notes in Computer Science, 2009, Volume 5646/2009, 207-215.
- L. Joshi K.E. Spaulding and G.J. Woolfe, “Using a residual image formed from a clipped limited color gamut digital image to represent an extended color gamut digital image,” US6301393B1, United States Patent and Trademark Office, 2000.
- Reinhard, M. Stark, P. Shirley, and J. Ferwerda. Photographic tone reproduction for digital images. ACM Trans. Graph., 21(3):267–276, July 2002.
- Durand and J. Dorsey. Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph., 21(3):257–266, July 2002.
- Li, L. Sharan, and E. H. Adelson. Compressing and companding high dynamic range images with subband architectures. ACM Trans. Graph., 24(3):836–844, July 2005.
- Meylan and S. Susstrunk. High dynamic range image rendering with a retinex-based adaptive filter. IEEE Transactions on Image Processing, 15(9):2820–2830, Sept. 2006.
- Farbman, R. Fattal, D. Lischinski, and R. Szeliski. Edgepreserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph., 27(3):67:1–67:10, Aug. 2008.
- H. Land and J. J. McCann. Lightness and retinex theory. Journal of the Optical Society of America, 61(1):1-11, January 1971.
- G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. Academic Press, New York, NY, 1978.
- https://davidstutz.de/retinex-theory-and-algorithm/ Last accessed 27-04-2020.
- K. P. Horn. Determining lightness from an image. Computer Graphics and Image Processing, 3(4):277-299, December 1974.
- Blake. Boundary conditions for lightness computation in mondrian world. Computer Vision, Graphics, and Image Processing, 32(3):314-327, 1985.
- Hojatollah Yeganeh, Zhou Wang, “Objective Quality Assessment of Tone-Mapped Images,” IEEE Transactions on Image Processing, vol. 22, No. 2, February 2013.
- Zhetong Liang1, Jun Xu1, David Zhang1, Zisheng Cao2, Lei Zhang, “A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping,” CVPR 2018.
- V. Satyanarayana Tallapragada, G. V. Pradeep Kumar, Jaya Krishna Sunkara, “Wavelet Packet: A Multirate Adaptive Filter for Denoising of TDM Signal”, International Conference on Electrical, Electronics, Computers, Communication, Mechanical and Computing (EECCMC), January 28-29, 2018.
- Jaya Krishna Sunkara, Kuruma Purnima, Suresh Muchakala, Ravisankariah Y, “Super-Resolution Based Image Reconstruction”, International Journal of Computer Science and Technology, vol. 2, Issue 3, pp. 272-281, September 2011.
- G.A.E. Satish Kumar, Jaya Krishna Sunkara, “Multiresolution SVD based Image Fusion”, IOSR Journal of VLSI and Signal Processing, vol. 7, Issue 1, ver. 1, pp. 20-27, Jan-Feb 2017. DOI: 10.9790/4200-0701012027.
- Jaya Krishna Sunkara, M Santhosh, C Suneetha, V. V. Satyanarayana Tallapragada, “Vector Quantization – A Comprehensive Study”, International Journal of Engineering Science Invention, vol. 7, Issue 5, May 2018, pp. 27-38.