Abstract: Aiming at the importance and key of dim small target detection in infrared image processing technology, a detection algorithm based on nonlinear anti noise estimation is proposed to solve the problem of dim small target detection with high reliability and robustness. Based on the traditional visual saliency algorithm and spatial distance processing method, the proposed method uses the nonlinear weighting method to estimate the target and background area. On the basis of not significantly reducing the signal-to-noise ratio of the target signal, the influence of isolated small noise points on the performance of the detection algorithm can be weakened, and the anti-noise performance can be improved. Firstly, the background is predicted by modular and nonlinear mapping, and then the distance correlation factor is integrated to filter out the noise interference. Finally, the binary threshold segmentation is carried out on the processed image to automatically detect and output the target position information to the next level processing software. The experimental results show that the proposed algorithm can obtain a higher detection rate on the subject test curve under the same false alarm rate and significantly suppress the background noise compared with the advanced weak and small target detection algorithm in recent years; On the test comparison data of local signal-to-noise ratio and background suppression factor, the proposed algorithm can obtain higher detection indexes. The disadvantage is that the algorithm adopts nonlinear processing technology and has low operation efficiency. It needs to further optimize the algorithm to improve the calculation speed and realize the real-time target detection of the algorithm.