French
English
CONTEXT:
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.
OBJECTIVE:
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].
ROAD MAP:
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.
SKILLS:
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++,...)
[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.