FR-GAN: A Fusion Refine with Dual Attention Architecture for Denoising of Dental Panoramic X-ray Images

Denoising dental panoramic X-ray images has become a major concern in medical imaging and computer vision, particularly when the Gaussian noise is high. To denoise the noisy images while preserving image features, we introduce a fusion-refine generative adversarial network (FR-GAN). The FR-GAN comprises a generator, a dual-attention U-Net, and a ResNet enhanced discriminator. The generator takes noisy images as input and produces coarse representations for initial reconstruction. Then, these coarse images are used as a preliminary denoised image, which is again refined to restore structural features for image clarity. In addition, we incorporate the U-Net with a dual-attention mechanism linking the generator and discriminator, which enables the refinement of coarse features into smooth features to create meaningful representations. Meanwhile, the U-Net refiner incorporates progressive feature fusion attention and low-rank local window attention to complement multi-scale feature extraction and local texture refinement. Lastly, the ResNet block-enhanced discriminator implements perceptual realism by differentiating genuine and reconstructed images and directing the network to yield high-quality images. In our experiment, 100 dental images collected from Al-Badar Dental College and Hospital, Kalaburagi, were subjected to Gaussian noise at 20 dB and 40 dB using ImageJ software. To evaluate the effectiveness of the proposed approach, experiments were conducted with Gaussian noise at 20 dB and 40 dB. The experimental results show that the proposed method is superior to other state-of-the-art approaches for Gaussian noise of 20 dB and 40 dB. It achieves a PSNR of 31.705, SSIM of 0.826, MSE of 0.0007, and MAE of 0.0195 at 40 dB, and a PSNR of 32.897, SSIM of 0.861, MSE of 0.0007, and MAE of 0.0184 at 20 dB. The framework provides effective high-quality denoising with clean edges, clear textures, and few artifacts, and is applicable to precision-critical tasks, such as medical and scientific imaging.