Digital holographic microscopy phase noise reduction based on an over-complete chunked discrete cosine transform sparse dictionary. (July 2023) - Lin, Zihan; Jia, Shuhai; Zhou, Xing; Zhang, Huajian; Wang, Longning; Li, Guojun; Wang, Zhe
Highlights: The large amount of random noise severely affects DHM imaging. In order to minimize the effect of random noise, an over-complete discrete cosine transform (DCT) sparse dictionary method is proposed. The results show that the overcomplete sparse dictionary has better sparse performance and can better remove the random noise from the holographic image. To further improve the noise reduction performance, a DHM phase noise reduction method based on an over-complete chunked DCT sparse dictionary is proposed. It chunked and smoothed the image to effectively exploit the correlation in the adjacent pixel points of the holographic image. The experimental results show that the reconstructed peak phase signal-to-noise ratio is improved from 53.1230 to 66.4436. The sample feature similarity is also improved from 0.8171 to 0.9377. The current noise reduction methods for the field of digital holographic microscopy have limitations. A certain method can only denoise the image for either phase map or intensity map. The new method proposed in this paper can perform good noise reduction in both categories of images. The results demonstrate that the new method achieves efficient processing efficiency during the noise reduction process and also takes into account the detail retention capability. Abstract: It is well known that a highly coherent laser is used as the illumination source in the digital holographic microscopy (DHM) recording optical path. Lighting sources and optical components may introduce a lot of coherent noise, which is marked as random noise, into the system. In addition, the sample characteristics and the experimental environment also introduce various random noises. Random noise severely degrades the quality and accuracy of the DHM imaging. To reduce the effect of random noise, this paper proposes a noise reduction method based on an over-complete chunked discrete cosine transform (DCT) sparse dictionary, also called a compressed sensing (CS) sparse representation reconstruction image method. The experimental results show that the reconstructed phase peak's signal-to-noise ratio (PSNR) improves from 53.1230 to 66.4436. The sample feature similarity (FSIM) improved from 0.8171 to 0.9377. The experimental data fully demonstrate that the proposed method can reduce the effects caused by random noise and effectively retain the sample details. Compared to traditional filter methods, it has advantages of noise reduction performance, detail retention performance and processing efficiency. Therefore, the proposed method in this paper will play a more important role in the DHM phase measurement.