In low-light environments, the signal-to-noise ratio of CMOS image sensors decreases significantly. In order to improve the imaging quality, this paper proposes a Transformer-based denoising method, IPDT. In the noise model, the effect of dark current noise on the low light images is emphasized. The denoising network contains the Du-al-Attention Transformer Block (DTB) module including spatial-channel attention and the prompt block (PB). The DTB captures global and detail information of low-light images through cross-window and cross-channel interac-tions, respectively. The PB acquires illumination information through learnable prompt parameters, dynamically guiding the denoising network. Experiments on See-in-the-Dark (SID) dataset and Smartphone Image Denoising Dataset (SIDD) datasets indicate that the proposed method effectively suppresses the noise of low-light images and demonstrates superior performance in terms of Blind Referenceless Image Spatial Quality Evaluator (BRISQUE) and Multi-Scale Structural Similarity Index (MS-SSIM) metrics.