10. Conclusion

In this chapter, a new image denoising technique is proposed. It combines two denoising approaches. The first one is a dual-tree discrete wavelet transform (DT-DWT)-based denoising technique, and the second one is a two-stage image denoising by principal component analysis with local pixel grouping (LPG-PCA). The first step of this proposed technique consists in applying the first approach to the noisy image in order to obtain a first estimate of the clean image. Then, we estimate the level of noise corrupting the original image. This estimation is performed by using a method of noise estimation from noisy images. The third step of the proposed technique consists in using this first clean image estimation, the noisy image, and this noise-level estimate as inputs of the second image denoising system (LPG-PCA-based image denoising) in order to obtain the final estimation of the clean image. A comparative study is performed between the proposed image denoising technique and two others denoising approaches where the first is based on DT-DWT and the second is based on LPG-PCA. This study is based on PSNR and SSIM computations, and the obtained results show that the proposed technique outperforms the two other denoising approaches. We also computed SNR (Signal to Noise Ratio) and MSE (Mean Square Error) and the obtained results also show that the proposed technique outperforms the others techniques.

#### Acknowledgements

We would like to thank all the people who contributed in some way to this work which was supported by the CRTEn (Center of Research and Technology of Energy) of Borj Cedria, Tunisia, and the Ministry of Higher Education and Scientific Research.
