High-quality image reconstruction plays an important part in histopathological image analysis, especially for HGSOC diagnosis, because of a great deal of fine cellular structures that should be clearly visible. In real scenarios, however, medical images usually face a series of problems due to acquisition limitations, which might obscure some significant diagnostic features. This work presents FUDA-NET, a new image denoising framework that enhances noisy histopathological images while maintaining the integrity of structure and texture. The architecture is based on an improved U-Net design integrated with a dual attention mechanism- Channel and Spatial attention, which enables the network to selectively emphasize meaningful features and suppress background noise. Additionally, a fuzzy logic layer is incorporated at the bottleneck to handle uncertainty and enhance contextual reasoning during feature extraction. This proposed FUDA-NET framework combines Mean Squared Error (MSE) and Structural Similarity Index Measures (SSIM) based loss function to ensure both pixel wise accuracy and perception similarity. Experiment conducted on 12,019 training images and 1188 testing images of High Grade Serous Ovarian Cancer, histopathological data set shows that FUDA-NET achieves superior denoising performance outperforming traditional and recent deep learning methods such as DnCNN, U-Net, U-Net with Attention and Noise2Noise in terms of PSNR, SSIM, MSE, MAE and FSIM. This approach contributes to improve visual clarity and diagnostic reliability in medical imaging.