A comparative study of CARE 2D and N2V 2D for tissue‐specific denoising in second harmonic generation imaging

This study explored the application of deep learning in second harmonic generation (SHG) microscopy, a rapidly growing area. This study focuses on the impact of glycerol concentration on image noise in SHG microscopy and compares two image restoration techniques: Noise‐to‐Void 2D (N2V 2D, no reference image restoration) and content‐aware image restoration (CARE 2D, full reference image restoration). We demonstrated that N2V 2D effectively restored the images affected by high glycerol concentrations. To reduce sample exposure and damage, this study further addresses low‐power SHG imaging by reducing the laser power by 70% using deep learning techniques. CARE 2D excels in preserving detailed structures, whereas N2V 2D maintains natural muscle structure. This study highlights the strengths and limitations of these models in specific SHG microscopy applications, offering valuable insights and potential advancements in the field . This study compares CARE 2D and N2V 2D deep learning methods for tissue‐specific denoising in SHG microscopy images. It shows CARE 2D's strength in detailed structure preservation and N2V 2D's ability to maintain natural muscle structure, providing key insights for tissue‐specific SHG image restoration.