Abstract
Under the condition of extremely under-sampling, the iterative algorithm based on the total variation(TV) constrain is common for the reconstruction of optical deflection tomography. In the algorithm, the minimization of TV is implemented by the gradient descent approach, and the constraints are performed by projection on convex sets (POCS). In this paper, we discuss the modulation factor of the gradient descent method, and propose a new adaptive modulation factor for gradient descent. Experiments were done on a series of modulation factor functions under different projection angles and noise environment, and the experimental results were compared and analysed. And the algorithm proposed in this paper is compared with the soft threshold filter TV minimization algorithm. The results demonstrate that the adaptive modulation factor proposed in this paper can automatically and continuously update the value of the modulation factor, reduce the reconstruction error and improve the reconstruction quality.
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