A performance impact of Andrew’s Sine threshold for a robust regularized SRR based on ML framework

Vorapoj Patanavijit; Kornkamol Thakulsukanant

Authors

Vorapoj Patanavijit Department of Electrical and Electronic Engineering, Vincent Mary School of Engineering, Assumption University of Thailand, Samuthprakarn 10540, Thailand
Kornkamol Thakulsukanant Department of Business Information Systems, Martin de Tours School of Management and Economics, Assumption University of Thailand, Samuthprakarn 10540, Thailand

Keywords:

SRR (Super-Resolution Reconstruction), DIR (Digital Image Reconstruction), DIP (Digital Image Processing), ML (Maximum Likelihood) Estimation, DSP (Digital Signal Processing)

Abstract

One of the most successful Digital Image Reconstruction (DIR) techniques for increasing image resolution and improving image quality is the Super-Resolution Reconstruction (SRR), which is the procedure of integrating a collection of aliased low-resolution low-quality images to form a single high-resolution high-quality image. However, the mainstream SRR algorithms are too delicate to noisy environments because these mainstream SRR algorithms are often comprised by the ML (L1 or L2) estimation techniques thereby the new robust SRR algorithm, which is comprised by Andrew’s Sine norm, has been proposed for dealing with noisy environments. Because the performance of the new SRR algorithm heavily relies on this Andrew’s Sine norm soft-threshold parameter, resultantly, this paper aims to investigate the impact characteristic of this norm constant parameter on the novel SRR algorithm. In addition, multitudinous experiments (which are applied on two standard images: Lena image and Susie image) are simulated to make the extensive results under five noise models: noise free, additive Gaussian noise, multiplicative Gaussian noise, Poisson noise and Impulsive noise with several noise powers for demonstrating the relationship between the SRR performance (in PSNR) and Andrew’s Sine norm soft-threshold parameter under each noisy cases.

References

Altunbasak, Y., Patti, A. J., & Mersereau, R. M. (2002). Super-resolution still and video reconstruction from MPEG-coded video. IEEE Transactions on Circuits and Systems for Video Technology 12(4), 217-226.

Black, M. J., Sapiro, G., Marimont, D., & Heeger, D. (1998). Robust anisotropic diffusion. IEEE Transactions on Image Processing 7(3), 421-432. DOI: 10.1109/83.661192

Elad, M., & Feuer, A. (1997). Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 6(12), 1646-1658. DOI: 10.1109/83.650118

Elad, M., & Feuer, A. (1999a). Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Transactions on Image Processing 8(3), 387-395. DOI: 10.1109/83.748893

Elad, M., & Feuer, A. (1999b). Super-resolution reconstruction of image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 817-834. DOI: 10.1109/34.790425

Elad, M., & Hel-Or, Y. (2001). A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur. IEEE Transactions on Image Processing 10(8), 1187-1193. DOI: 10.1109/83.935034

Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004a). Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 14(2), 47-57.

Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004b). Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 13(10), 1327-1344. DOI: 10.1109/TIP.2004.834669

Farsiu, S., Elad, M., & Milanfar, P. (2006). Multiframe demosaicing and super-resolution of color images. IEEE Trans. on Image Processing 15(1), 141-159. DOI: 10.1109/TIP.2005.860336

Kang, M. G., & Chaudhuri, S. (2003). Super-resolution image reconstruction. IEEE Signal Processing Magazine 20(3), 19-20.

Ng, M. K., & Bose, N. K. (2003). Mathematical analysis of super-resolution methodology. IEEE Signal Processing Magazine 20(3), 62-74. DOI: 10.1109/MSP.2003.1203210

Park, S. C., Park, M. K., & Kang, M. G. (2003). Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 20(3), 21-36. DOI: 10.1109/MSP.2003.1203207

Patanavijit, V. (2008). Andrew’s Sine estimation for a robust iterative multiframe super-resolution reconstruction using stochastic regularization technique. Proceeding of IEEE Northeast Workshop on Circuits And Systems (IEEE-NEWCAS-TAISA'08), Montreal, Canada, June 2008.

Patanavijit, V. (2009a). Super-resolution reconstruction and its future research direction, AU Journal of Technology (AU J.T.), Assumption University of Thailand, Bangkok, Thailand, Jan. 12(3), 149–163.

Patanavijit, V. (2009b). Mathematical analysis of stochastic regularization approach for super-resolution reconstruction, AU Journal of Technology (AU J.T.), Assumption University of Thailand, Bangkok, Thailand, April. 12(4), 235–244.

Patanavijit, V. (2009c), A robust iterative multiframe SRR based on Andrew’s Sine stochastice with Andrew’s Sine-Tikhonov regularization, Proceeding of IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2008), Bangkok, Thailand, 8-11 Feb. 2009. Page(s): 1-4. DOI: 10.1109/ISPACS.2009.4806736

Patanavijit, V. (2009d), Video enhancement using a robust iterative SRR based on Andrew’s Sine regularization technique, Proceeding of IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2009), Kanazawa, Japan, 7-9 Jan. 2009. Page(s): 115-118. DOI: 10.1109/ISPACS.2009.5383890

Patanavijit, V. (2011), A robust recursive SRR based on Andrew’s Sine stochastic estimation with fast affine block-based registration for video enhancement, Proceedings of The 34th Electrical Engineering Conference (EECON-34), Ambassador City Jomtien Hotel, Pataya, Chonburi, Thailand, Dec.

Patanavijit, V. (2013), Computational Tutorial of Steepest Descent Method and Its Implementation in Digital Image Processing, ECTI E-magazine, ECTI Association, Bangkok, Thailand, Vol. 7, No. 1, Jan. – Mar. (http://www.ecti-thailand.org/emagazine/) (ISSN: 2286-7074)

Patanavijit, V. (2015). Comparative experimental exploration of robust norm functions for iterative super resolution reconstructions under noise surrounding, ECTI Transactions on EEC (Electrical Engineering/ Electronics and Communications), 13(2), 83-91. ECTI Association, Thailand.

Patti, A. J., & Altunbasak, Y. (2001). Artifact reduction for set theoretic super resolution image reconstruction with edge constraints and higher-order interpolation. IEEE Transactions on Image Processing 10(1), 179-186.

Rajan, D., Chaudhuri, S., & Joshi, M. V. (2003). Multi-objective super resolution concepts and examples. IEEE Signal Processing Magazine 20(3), 49-61. DOI: 10.1109/MSP.2003.1203209

Rajan, D., & Chaudhuri, S. (2003). Simultaneous estimation of super-resolution scene and depth map from low resolution defocuses observations. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1102-1117. DOI: 10.1109/TPAMI.2003.1227986

Schultz, R. R., & Stevenson, R. L. (1994). A Bayesian approach to image expansion for improved definition. IEEE Transactions. on Image Processing 3(3), 233-242. DOI: 10.1109/83.287017

Schultz, R. R., & Stevenson, R. L. (1996). Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 5(6), 996-1011. DOI: 10.1109/83.503915