Differential Phase Contrast (DPC) imaging plays an important role in the family of quantitative phase measurement. However, the reconstruction algorithm for quantitative DPC (qDPC) imaging is not yet optimized, as it does not incorporate the inborn properties of qDPC imaging. In this research, we propose a simple but effective image prior, the Dark-field Sparse Prior (DSP), to facilitate the phase reconstruction quality for all DPC-based phase reconstruction algorithms. The DSP is based on the key observation that most pixel values for an idea differential phase contrast image are zeros since the subtraction of two images under anti-symmetric illumination cancels all background components. With this DSP prior, we formed a new cost function in which L0-norm was used to represent the DSP. Further, we developed the algorithm based on the Half Quadratic Splitting to solve this NP-hard L0-norm problem. We tested our new model on both simulated and experimental data and compare it against State-Of-The-Art (SOTA) methods including L2-norm and total variation regularizations. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, which significantly increases the experimental robustness, while maintaining the data fidelity. In general, the DSP supports high-fidelity qDPC reconstruction without any modification of the optical system, which simplifies the system complexity and benefit all qDPC applications.
We propose the combination of single image blind deconvolution and illumination correction (BDIC) to enhance the image quality of a microscopy system. We evaluated the performance this method by calculating the peak signal-to-noise ratio and structural similarity of both raw and enhanced images with respect to the reference images. Both subjective and objective assessments show that BDIC increases the image's quality including its contrast and signal-to-noise ratio, without losing image resolution and structural information. To demonstrate its applicability, we also applied BDIC to different samples including plant root tissue and human blood smears.
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