Image denoising has made remarkable strides over the years, primarily focusing on reconstructing a clean image from a single degraded input. However, in practical applications, burst image denoising demonstrates promising potential for restoring higher-quality images from burst sequences, particularly relevant for burst photography. In this paper, we pioneer the establishment of a new real-world burst denoising benchmark, named RealBDN, which consists of noisy image bursts and the corresponding noise-free/noise-low images. To tackle the challenges of real-world burst denoising, we propose a Dual-Branch Burst Denoising (DBBD) network to investigate the real-world noise among image bursts in the Noise-Learning Branch for facilitating image denoising in the decoding branch. Our DBBD specifically employs a homography alignment and a feature extractor to separately extract noise features and image features. These noise features are then processed to derive fused noise features and generate an estimated noise map within the Noise-Learning Branch. The fused noise features serve as a guide for the Decoding Branch during the restoration process. Moreover, we have conducted extensive experiments on the RealBDN dataset. The results demonstrate the superior performance of our DBBD, as evidenced by both quantitative and qualitative measures. In the interest of promoting further research and development in this field, we will publicly release our dataset, trained model, and source codes.