The phase recovery algorithm based on the transport of intensity equation uses the fast Fourier solution to calculate the phase from the acquired intensity, but the solution accuracy is not high, and there will be instability caused by zero points and minimum points. Aiming at this problem, An improved fast Fourier solution based on the intensity transfer equation is proposed. By finding a suitable constant value to replace the focused intensity value in the traditional formula, the initial guess solution of the phase is solved; the initial phase and the focused intensity form a new complex amplitude, and then a new intensity differential is obtained in the form of angular spectrum propagation, and then the new The intensity differential of is substituted into the phase solution formula to obtain a new phase, so as to iteratively optimize the phase; when the iteration converges, the exact solution of the phase can be obtained. This solution can bypass the instability caused by the zero point and the minimum value point and has the advantage of high precision. Keywords: Transport of intensity equation, Intensity differential, Iterative optimization, Angular spectrum propagation, Fast Fourier solution, phase recovery.
In the dual-camera phase retrieval method, the phase is solved by positive- and negative- defocusing images obtained through a single exposure after dual cameras are installed on an inverted microscope. However, due to the installation error of the cameras, translation and rotation of images exist between the images, resulting inaccurate phase retrieval results. In this paper, we proposed a dual-camera phase retrieval method based on fast adaption image restoration and transport of intensity equation. Firstly, let the positive-defocusing image be the reference image. Then using the fast adaption image restoration algorithm to find the texture information in order to find best matching block quickly. According to the number of high frequency information of the block, the size of block can be defined in order to increase the precision and speed of the restoration. After that, priority can be change as the sum of two parts, which can avoid the situation of 0 priority. Then, burring the boundary point of restored image in order to reduce the block effect. Finally, the transport of intensity equation can be used in phase retrieval results. Comparing with the normal algorithm, this method can restore the image much better.
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