Enhancing U-Net for image denoising with bilateral filter noise residue and gradient estimation (BIRUNet)

In recent years, Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks, including image denoising. Image denoising focuses on reconstructing a clean image from its noise-corrupted counterpart. In this paper, we propose BIRUNet, a bilateral-filter-based noise-residue U-Net enhanced with gradient estimation. The objective of this research is to improve the learning capability of the traditional U-Net by integrating manually derived image priors. Although several improved U-Net variants exist, many suffer from high computational cost and rely solely on learned noise patterns, which limits their reconstruction quality. To address these issues, BIRUNet incorporates two additional priors: (i) noise residue extracted using a traditional bilateral filter, and (ii) gradient information derived from the input image. These priors are concatenated with the noisy grayscale image and fed into an encoder-decoder U-Net architecture to generate a more accurate denoised output. The proposed model is evaluated both quantitatively and visually across multiple datasets. With a particular focus on preserving edge details, SSIM values are compared against those of more complex models, demonstrating superior performance. BIRUNet achieves a PSNR of 26.66 dB at a high noise level (σ = 50), confirming its effectiveness in challenging denoising scenarios.