Recrutement en cours/passé: 
Recrutement en cours
Clinical ultrasound images are often analyzed in in challenging conditions as one is confronted to speckle noise and blurring. Enhancing these images can help both help the practitioners for a better interpretation and be a pre-processing step for further tasks such as segmentation and registration. Recently, in a series of works, [1][2], we proposed a method to called wavelet-fisz (WF) despeckling which aims at removing speckle from US images. This method has proved to be competitive with state-of-the-art methods and enjoys adaptability and easy-tuning. However, the obtained images (cf. Figure in the PDF file) are often still blurred. The aim of this project is to Improve the resolution of the WF algorithm results.
The purpose of this internship is to extend WF to perform jointly speckle removal and deconvolution. In particular, the student will explore the characteristics of the point-spread function (PSF) in the wavelet-domain [3] and propose a scheme that enables to solve the despeckling-deconvolution problem through wavelet-thresholding [4].
1/ Understanding the wavelet-thresholding paradigm, the WF technique and the behavior of convolution operators in the wavelet-domain through the existing literature.
2/ Characterization of the PSF and its wavelet decomposition.
3/ Constructing a scheme for coupling despeckling and deconvolution.
4/ Validation of the algorithm on simulated and real data.
5/ Writing a scientific report on the results in English.
Potential applicants are required to have a strong knowledge in signal / image processing or/and applied mathematics. Basic knowledge on wavelet processing is likable but not necessary. The student is free to use any programming language (Matlab, Python, C++,...)
[1] Y. Farouj, J.M. Freyermuth, L. Navarro, M. Clausel, P. Delachartre, Hyperbolic Wavelet-Fisz denoising for a model arising in Ultrasound Imaging. IEEE Trans. Comp. Imag. (2017).
[2] Y. Farouj, J.M. Freyermuth, L. Navarro, M. Clausel, P. Delachartre, Ultrasound Spatio-temporal Despeckling via Kronecker Wavelet-Fisz Thresholding. Under review (2017).
[3] Yves Meyer, Ondelettes et opérateurs.
[4] Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. biometrika, 425-455.