Abstract: Abstract: Noise pollution on locust micro-section images is always unavoidable during the acquisition of the images. However, few researches have been devoted to the de-noise processing of locust section images. The locust section image is generally characterized by rich textures, smooth regions and well-defined edges. Since the textures, the edges and noises of the images are high-frequency components, wavelet transformation can't successfully get rid of noise on the images effectively without destroying the edge features, i.e., it might cause the pseudo-Gibbs' effect and edge blurring. Since the gradient value of the textures is small while the gradient value of the edges and noises is large, partial differential equation (PDE) diffusion can't successfully get rid of noise on the images effectively without destroying the texture, i.e., it tends to lose the original textural details. Therefore, we proposed a new algorithm for the de-noise of locust section image, which was called adaptive wavelet PDE method. It possessed all the advantages of wavelet decomposition and anisotropic diffusion. It could remove noises successfully with the textural details preserved and the edges clear. The procedure of the proposed algorithm included 2 steps as follows. First, we de-noised the images using the sym5 wavelet soft-threshold algorithm, in which the wavelet decomposition level was adaptively selected according to the PSNR (peak signal to noise ratio) value of the de-noise images and the soft-threshold was obtained by the Birge-Massart penalty algorithm. Further de-noising was done with the Perona Malik (PM) model, in which the iterations were adaptively selected according to the PSNR value of the de-noise images, and the gradient threshold according to the 2-norm of the image grey value. After the implementation of the adaptive wavelet PDE algorithm, a 3-step simulation test was made to evaluate the effectiveness of the proposed algorithm using MATLAB 8.2. In order to determine the optimal wavelet decomposition level for the image, we compared the image de-noising results on different wavelet decomposition levels. The experiments showed that wavelet decomposition level should be 2 while using the wavelet soft-threshold for the de-noise image. Then, to determine the optimal iterations for the PM model, the de-noise results in different iterations were compared with each other. The experiments showed that the iterations between 5 and 10 (inclusively) were appropriate while using the PM model for the de-noise image. Finally, the proposed algorithm had some comparison with the conventional de-noise algorithms. The de-noised image obtained by the proposed algorithm was less residual noise and clearer textures than other algorithms visually. We used 2 common de-noise evaluation criteria of image, i.e. PSNR and structural similarity image measurement (SSIM), which measured the degree of image distortion and similarity between the processed and the original image. The PSNR value of de-noise image obtained by the proposed algorithm was 28.7474 dB, which was higher than using the Wiener filtering, the median filtering, the wavelet threshold de-nosing and the PM Model de-nosing, by 2, 3, 2 and 1 dB, respectively. It was higher than the PSNR value of the noisy image by 4.6 dB. The SSIM value of de-noise image using the proposed algorithm was 0.8258, which was the largest among the above-mentioned algorithms and this indicated the de-noise image using the proposed algorithm was closer to the original image in the brightness, contrast and structure aspects. In conclusion, the proposed algorithm is feasible and effective for de-noising locust section image. It will provide technical support to the subsequent processing of the image, which will bring convenience to better understand the structure of locust cells and nerves and hence be helpful to reduce pollution resulting from the abuse of chemical pesticides ultimately.