HIST-DIP: histogram thresholding and deep image priors assisted smartphone-based fluorescence microscopy imaging

Abstract

Portable fluorescence microscopes coupled with smartphones offer accessible and cost-effective point-of-care diagnostic solutions, but often produce noisy and blurry images with poor contrast. Here, we introduce HIST-DIP (HIStogram Thresholding and Deep Image Prior), an unsupervised framework for fluorescence microscopy image restoration. Histogram thresholding isolates fluorescence signals by removing background noise, while DIP refines structural details and enhances resolution without large labeled datasets. Validation results show substantial quality gains including the average Peak Signal-to-Noise Ratio (PSNR) improved from 15.59 dB to 27.10 dB, and the Structural Similarity Index Measure (SSIM) rose from 0.035 to 0.82. Contrast-to-noise ratio (CNR) and signal difference-to-noise ratio (SDNR) also increased significantly, indicating sharper bead outlines and reduced background interference. Unlike conventional deep learning methods, HIST-DIP needs no external training data, making it well-suited for real-time, low-cost, and point-of-care diagnostic imaging. These findings highlight the potential of HIST-DIP in enhancing the quality of smartphone-based microscopy images, while also motivating future research towards optimizing the methods for real-time on-device computations.