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-DnCNN that fuses wavelet preprocessing with a dual-branch Transformer–CNN architecture. Wavelet decomposition separates high- and low-frequency components for targeted noise reduction; the CNN branch restores local details while the Transformer branch captures global context; and an adaptive loss balances quantitative fidelity with perceptual quality. On the Fluorescence Microscopy Denoising (FMD) benchmark, our method surpasses leading CNN- and Transformer-based approaches, improving PSNR by 2.34% and 0.88% and SSIM by 0.53% and 1.07%, respectively. This framework offers enhanced generalization and practical gains for fluorescence image denoising.