Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure times result in lots of noise in the blurry image. Although existing blind deblurring methods produce satisfactory results on blurred images with little or no noise, they struggle to handle high noise levels. Strong noise compromises the accuracy of the estimated kernel and significantly reduces the quality of the deblurring results. To address this challenge, we propose a Residual Guidance Strategy (RGS) to suppress the influence of noise. Our method leverages adjacent coarser-scale information in the image pyramid to guide the blur kernel estimation in the current scale. Therefore, for blurred images with unknown noise levels and types, our method still estimates more accurate blur kernels, which are essential for subsequent non-blind restoration. Extensive experiments on both synthetic and real datasets have demonstrated that our method consistently outperforms numerous state-of-the-art methods under high levels of noise quantitatively and qualitatively.