Image Denoising Using Wavelet Transform and CUDA


  • Hovhannes M. Bantikyan State Engineering University of Armenia (Polytechnic)


Image denoising, Discrete Wavelet Transform, Haar wavelet, Daubechies wavele, Parallel computing, GPGPU, CUDA programming


The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding to represent a discrete signal in a more redundant form, often as a preconditioning for data compression. Beginning in the 1990s, wavelets have been found to be a powerful tool for removing noise from a variety of signals (denoising). In this paper the implementation of DWT (Discrete Wavelet Transform)-based denoising algorithm in parallel manner on Graphics Processing Unit is presented, using the CUDA technology.


W. J. van der Laan, C. J. Andrei and J. B. T. M. Roerdink, “Accelerating wavelet lifting on graphics hardware using CUDA”, Parallel and Distributed Systems, IEEE Transactions, vol. 22, pp. 132--146, 2011.

(2012) R. Cohen, “Signal denoising using wavelets”, [Online]. Available:

H. Om and M. Biswas, “A new image denoising scheme using soft-thresholding”, Journal of Signal and Information Processing, vol. 3, pp. 360--363, 2012.

P. Porwik and A. Lisowska, “The Haar–wavelet transform in digital image processing: its status and achievements”, Machine Graphics and Vision, vol. 13, pp.79--98, 2004.

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage”, Biometrika, vol. 81, pp. 425--455, 1994.

I. W. Selesnick, Wavelet Transforms - A Quick Study, Physics Today magazine, September 27, 2007.

(2012) NVIDIA CUDA C Programming Guide, NVIDIA Corp, [Online]. Available:

J. Sanders and E. Kandrot, CUDA by Example, Addison-Wesley, 2010.

“Wavelet Properties Browser”, [Online]. Available:




How to Cite

Bantikyan, H. M. . (2021). Image Denoising Using Wavelet Transform and CUDA. Mathematical Problems of Computer Science, 41, 122–130. Retrieved from