Abstract
Fourier Ptychographic Microscopy (FPM) allows high resolution imaging using iterative phase retrieval to recover an estimate of the complex object from a series of images captured under oblique illumination. FPM is particularly sensitive to noise and uncorrected background signals as it relies on combining information from brightfield and noisy darkfield (DF) images. In this article we consider the impact of different noise sources in FPM and show that inadequate removal of the DF background signal and associated noise are the predominant cause of artefacts in reconstructed images. We propose a simple solution to FPM background correction and denoising that outperforms existing methods in terms of image quality, speed and simplicity, whilst maintaining high spatial resolution and sharpness of the reconstructed image. Our method takes advantage of the data redundancy in real space within the acquired dataset to boost the signal-to-background ratio in the captured DF images, before optimally suppressing background signal. By incorporating differentially denoised images within the classic FPM iterative phase retrieval algorithm, we show that it is possible to achieve efficient removal of background artefacts without suppression of high frequency information. The method is tested using simulated data and experimental images of thin blood films, bone marrow and liver tissue sections. Our approach is non-parametric, requires no prior knowledge of the noise distribution and can be directly applied to other hardware platforms and reconstruction algorithms making it widely applicable in FPM.
Highlights
Fourier Ptychographic Microscopy (FPM) is a recently developed computational imaging technique that significantly increases the information gathering power of a light microscope
Due to a combination of the angular dependence of the radiant intensity of LEDs typically used in FPM systems [10] and the directional scattering properties of the sample, the signal to noise ratio (SNR) decreases with increasing inclination angle
To test the performance of the Structure-dependent Amplification (SdA) method we computed a set of LR FPM images from a complex object based on an HR image of part of a USAF resolution test target (Fig. 5(a)), where the phase of the object was set to the amplitude scaled between 0 and π
Summary
Fourier Ptychographic Microscopy (FPM) is a recently developed computational imaging technique that significantly increases the information gathering power of a light microscope. Due to a combination of the angular dependence of the radiant intensity of LEDs typically used in FPM systems [10] and the directional scattering properties of the sample, the SNR decreases with increasing inclination angle As a result, this simple method tends to be unsuited to removing the background in DF captured with off axis illumination angles, which leads to an inevitable trade-off between image artefacts resulting from insufficient background removal and the loss of the high spatial frequency information associated with these displaced passbands. We used the same simulated FPM image data to investigate the suitability of adaptive methods for removing background in raw DF images, finding that, as with global methods, we were unable to obtain good quality reconstructed images An example of this is shown in Fig. 1(c4) in which the background in each DF image was removed using the rolling ball algorithm [13] prior to reconstruction, where the rolling ball radius was set to a value which gave the best result
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