Abstract: Considering the limited computing resources of the mobile terminal, this paper adopts the U-shaped network as the backbone network of image denoising, and proposes a new real image denoising algorithm CBDNet+. On the basis of CBDNet, it is proposed to use wavelet transform in the up-sampling and down-sampling stages, which reduces the utilization of multipliers and is easier to implement on mobile terminals with limited resources, and the image denoising performance has a certain improvement compared with CBDNet. To meet the requirements of limited resources and low power consumption, the trained network is pruned and 8-bit quantized and compressed, which effectively improves the efficiency of the algorithm and reduces the required storage space. On the basis of the algorithm, the research and design of the mobile terminal-specific neural network accelerator are carried out in terms of hardware architecture, on-chip cache, performance and power consumption. For the convolutional neural network image denoising algorithm using wavelet transform and wavelet inverse transform, a dedicated convolutional neural network accelerator structure is used to reduce the storage bandwidth on and off the chip; the parallel operation is used to improve the operation efficiency of the wavelet inverse transform; Under the premise of taking into account resources and speed, the accelerated reasoning of the algorithm is realized. A real image denoising system is implemented on the AX7350 ZYNQ platform. The results show that the system has an average computing performance of 55.2 GOPS and a power consumption of 1.93 W under a clock of 100 MHz. The image denoising system is tested on the DND test set, the signal-to-noise ratio is 36.21 dB, and the structural similarity ratio is 0.9435.