Fluorescence microscopy image denoising via a wavelet-enhanced transformer based on DnCNN network

Fluorescence microscopy is indispensable in life science research, yet denoising remains challenging due to varied biological samples and imaging conditions. We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer–convolutional neural network (CNN) architecture. Wavelet decomposition separates high- and low-frequency components for targeted noise reduction; the CNN branch restores local details, whereas the transformer branch captures global context; and an adaptive loss balances quantitative fidelity with perceptual quality. On the fluorescence microscopy denoising benchmark, our method surpasses leading CNN- and transformer-based approaches, improving peak signal-to-noise ratio by 2.34% and 0.88% and structural similarity index measure by 0.53% and 1.07%, respectively. This framework offers enhanced generalization and practical gains for fluorescence image denoising.