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

## TL;DR

This paper introduces BIRUNet, an enhanced U-Net model for image denoising that uses bilateral filter noise residue and gradient estimation to improve performance.

## Contribution

The novelty lies in integrating bilateral filter noise residue and gradient estimation into U-Net for better denoising.

## Key findings

- BIRUNet achieves a PSNR of 26.66 dB at high noise levels (σ = 50).
- The model outperforms complex models in preserving edge details with higher SSIM values.

## Abstract

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.