Section: New Results

Design of deconvolution algorithms for low exposure fluorescence microscopy images

Participants : Deepak George Skariah, Charles Kervrann.

Fluorescence imaging is popular in cell biology research due to its high contrast imaging capability. In microscopy imaging under low exposure conditions, the image quality is limited by out-of-focus blur and high noise. As a result a preprocessing stage known as deconvolution is needed to estimate a good quality version of the observed image. We proposed to design an efficient deconvolution algorithm for fluorescence microscopy under low exposure conditions by using the Poisson noise model. The result of deconvolution depends heavily on the choice of the regularization term. The regularization functional should be designed to remove noise while retaining the image structure. The choice of Poisson noise model and new regularization functional demands the design of a new and efficient optimization algorithm. We proposed to use a complex non quadratic regularization functional along with Poisson noise assumption for the first time. The use of non quadratic regularization makes the resulting optimization problem a complex one. This demanded the development of a problem-specific optimization algorithm which is fast as well as robust enough to minimize a non quadratic cost function. The use of non quadratic regularization together with Poisson noise model ensures that finer details of underlying structures are well restored in the presence of large amount of noise.

Collaborator: Muthuvel Arigovindan (Imaging Systems Lab, Department of Electrical Engineering, Indian

                         Institute of Science, Bangalore, India).