Physical prior-guided deep learning for SIM reconstruction: modeling object-to-image degradation.

Structured illumination microscopy (SIM) provides an enhanced spatial resolution of up to twice the conventional capacity. Recently, many approaches have attempted to combine deep learning frameworks with SIM reconstruction for better capability. Nonetheless, the inadequacy of training samples highlights the challenge of limited credibility and low generalization ability of deep learning, thus significantly constraining the application in biology.

Enhanced denoising for weak signal preservation in structured illumination microscopy.

Structured illumination microscopy (SIM) is a powerful super-resolution technology in biological science because of its fast imaging speed, low phototoxicity, and full-field imaging. Despite this, SIM is hampered by out-of-focus background noise, which can obscure weak fluorescence signals and render them unrecognizable. Previous denoising algorithms tended to eliminate the noise along with the weak signals, causing a decrease in image quality.