Fourierptychographic microscopy (FPM) is an imaging technique for reconstructing high-resolution images using low-resolution images acquired from a set of different angles of incident light. This technique can bypass the resolution limit of employed optics. The FPM algorithm comprises two main theoretical bases. The first one is the phase retrieval technique

which was originally developed for electron imaging. This technique is used to recover the lost phase information using intensity measurements

and it typically consists of alternating enforcement of the known information of the object in the spatial and Fourier domains. The second one is the aperture synthesis. This technique was originally deve-loped for radio astronomy to pass the resolution limit of the single radio telescope. The basic idea of this technique is to combine images from a collection of telescopes in the Fourier domain to improve the resolution. By integrating the two techniques

the FPM can transform a conventional microscope into a high-resolution

wide field-of-view one. The difference between the low-resolution image and the high-resolution image in the frequency domain is reflected in the energy in the high-frequency band

and the high-frequency energy is abundant in the high-resolution image. However

the energy in the high-frequency band reconstructed by the former algorithm remains small. This study proposes a new iterative updating mode of FPM-band energy adjustment in FPM (BE-FPM) to solve the problem. This method is based on the energy distribution of Fourier space in high-resolution images. The entire iteration process for every image is divided into two steps. The first step conducts the recovery depending on the concepts of conventional FPM

which is to update the sub-region of the Fourier spectrum by the recorded low-resolution images. The second step is to use the new updating mode

namely

band energy adjustment in the iterative process. Energy distribution of a high-resolution image

which is calculated from a similar high-resolution sample

is applied as the prior. The Fourier spectrum is divided into several bands. Every band has different frequency ranges. The energy of each band is calculated and adjusted by the high-resolution prior. The reconstructed image is brought closer to the high-resolution image by adjusting the energy of different frequency bands. After the iterative process for one image

the process is conducted for every captured low-resolution image several times until the convergence is achieved. Experimental results on resolution board and bean hole data demonstrate that the BE-FPM further improves the resolution of the reconstructed image and can highlight the edge information. We conduct the experiments on resolution board and bean hole data. Compared with the updating mode used in embedded pupil function recovery for FPM (EPRY-FPM) and the BE-FPM updating mode

the BE-FPM mode can further improve the resolution of the reconstructed image and highlight the edge information. The element of group eight in the resolution board has a better and clearer reconstruction effect in the BE-FPM reconstruction result. The boundary of bean hole achieves a much clearer reconstruction by using the BE-FPM. Gaussian noise and salt-and-pepper noise are added to the originally captured low-resolution images to prove the robustness of the BE-FPM. A reconstruction of the noisy images using the EPRY-FPM and BE-FPM proves that the robustness of the BE-FPM for noise is better than that of the EPRY-FPM. This paper presents a new iterative updating mode of FPM

namely

BE-FPM. Experiments on resolution board and bean hole data show that the BE-FPM updating mode can further improve the resolution of the reconstructed image and highlight the edge information. The BE-FPM updating mode is more robust than the EPRY-FPM when the recorded images contain noise. In biological samples

numerous images have similar distributions

and these samples have similar energy distributions in the Fourier space. Therefore

the BE-FPM has potentials in reconstructing an entire sample using a partial high-resolution image and reconstructing samples in the same class via a single high-resolution image.