Section: New Software and Platforms

SparseVolution

Sparse Variation for 2D Image Decovolution

Keywords: 2D - Fluorescence microscopy - Image processing - Problem inverse - Deconvolution

Functional Description: In order to improve the resolution of acquired fluorescence images, we introduced a method of image deconvolution by considering a family of convex regularizers. The considered regularizers are generalized from the concept of Sparse Variation which combines the L1 norm and Total Variation (TV) to favors the colocalization of high-intensity pixels and high-magnitude gradient. The experiments showed that the proposed regularization approach produces competitive deconvolution results on fluorescence images, compared to those obtained with other approaches such as TV or the Schatten norm of Hessian matrix. The final deconvolution algorithm has been dedicated to large 2D 20 000 x 20 000 images. The method is able to process a 512 x 512 image in 250 ms (Matlab) with a non optimized implementation.

• Participants: Hoai Nam Nguyen and Charles Kervrann

• Partner: Innopsys

• Contact: Charles Kervrann