DRB-Net: Dilated Residual Block Network for Infrared Image Restoration

Abstract

Infrared (IR) spectroscopic imaging offers label-free visualization of sample heterogeneity via spatially localized chemical information. This spatial-spectral data set is amenable to computational algorithms that highlight functional properties of the sample. Although Fourier transform IR (FT-IR) imaging provides reliable analytical information over a wide spectral profile, long data acquisition times are a major challenge impeding broad adoptability. Discrete frequency (DF) IR imaging is considerably faster, first by reducing the total number of spectral frequencies acquired to only those necessary for the task, and second by using substantially higher optical power via IR lasers. Further acceleration of imaging is hindered by high laser noise and usually relies on time-consuming averaging of ensemble measurements to achieve useful signal-to-noise ratio (SNR). Here, we develop a novel convolutional neural network (CNN) architecture capable of denoising discrete frequency infrared (DFIR) images in real-time, removing the need for excessive co-averaging, thereby reducing the total data acquisition time accordingly. Our architecture is based on dilated residual block network (DRB-Net), which outperforms state-of-the-art CNN models for image denoising task. To validate the robustness of DRB-Net, we demonstrate its efficacy on various unseen samples including SU-8 targets, polymers, cells, and prostate tissues. Our findings demonstrate that DRB-Net recovers high-quality data from noisy input without supervision and with minimal computation time.