Whole slide imaging (WSI) has recently been cleared for primary diagnosis in the U.S. A critical challenge of WSI is to perform accurate focusing in high speed. Traditional systems create a focus map prior to scanning. For each focus point on the map, a sample needs to be static in the x-y plane, and axial scanning is needed to maximize the contrast. Here we report a novel focus map surveying method for WSI. In this method, we illuminate the sample with two LEDs and recover the focus points based on 1D autocorrelation analysis. The reported method requires no axial scanning, no additional camera and lens, works for stained and transparent samples, and allows continuous sample motion in the surveying process. By using a 20× objective lens, we demonstrate a mean focusing error of ∼0.08  μm in the static mode and ∼0.17  μm in the continuous motion mode. The reported method may provide a turnkey solution for most existing WSI systems due to its simplicity, robustness, accuracy, and high speed. It may also standardize the imaging performance of WSI systems for digital pathology and find other applications in high-content microscopy, such as time-lapse live-cell imaging.

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In order to estimate fog density correctly and to remove fog from foggy images appropriately, a surrogate model for optical depth is presented in this paper. We comprehensively investigate various fog-relevant features and propose a novel feature based on the hue, saturation, and value color space, which correlate well with the perception of fog density. We use a surrogate-based method to learn a refined polynomial regression model for optical depth with informative fog-relevant features, such as dark-channel, saturation-value, and chroma, which are selected on the basis of sensitivity analysis. Based on the obtained accurate surrogate model for optical depth, an effective method for fog density estimation and image defogging is proposed. The effectiveness of our proposed method is verified quantitatively and qualitatively by the experimental results on both synthetic and real-world foggy images.