Abstract: Abstract: Wildlife monitoring images can be used to conduct accurate estimation of species diversity, quantity and inhabit attribution, offering scientific proofs for wildlife resource conservation.However, the quality and availability of acquired wildlife monitoring images were usually weakened due to different illumination variations in wild environments. To address this drawback, an adaptive image enhancement method based on Retinex theory was proposed in this paper.We utilized wildlife monitoring images collected at Saihanwula Nature Reserve in Inner Mongolia as experimental samples.These monitoring images were acquired from several field experimentsfrom 2010 to 2014 by using the infrared trigger cameras and they were classified into 4 illumination conditions, namely sufficient illumination condition, low illumination condition, shadow condition and overexposure condition. Firstly, we analyzed the pseudo halo phenomenon in illuminance component image estimation process caused by the traditional guided filter. The global smoothing factor of guided filtercan notbalance the halo elimination and image detail information preservation in illumination abruptchanging image regions. Therefore, we introduced the composite gradient of images to improve the guided filter algorithm. By calculating the composite gradient image, the local adaptive smoothing factor of the guided filter was obtained to achieve the joint optimal performances of the pseudo halo elimination and the dynamic range compression. In view of the over enhancement problem in conventional Retinex algorithm, a contrast adaptive stretching method based on Otsu threshold was then proposed to realize the correction of illumination component. By calculating the Otsu threshold in illumination component images, the estimated illumination component images could reach the optimal brightness extension effect at the threshold and realize the brightness improvement in dark image regions, and it could limit the over enhancement degree of the bright image regions.And it enhanced the adaptability of the algorithm to different illumination conditions. Lastly, in order to maintain the color information of enhanced images, the single channel illumination component of the corrected images and the 3 color channels of the original images were used to conduct separate calculation to maintain the correlation of the 3 color channels. It was validated that it did not increase the complexity of the algorithm. In order to prove the superiority of this algorithm, 50 wild animal monitoring images were selected randomly for validation. This algorithm was compared with MSRCR(multi-scale Retinex with color restoration) algorithm, bilateral filter Retinex algorithm and guided filter Retinex algorithm to test its quality performance of image enhancement. Compared with the other 3 algorithms, the average hue fidelity was increased by 81.00%,5.24% and 3.58%, respectively; the average information entropy was increased by 6.76%,6.23% and 2.61%, respectively; the average PSNR (peak signal to noise ratio) was improved by 53.43%,5.36% and -2.85%, respectively; the running time was reduced by -29.03%,78.51% and 28.68% respectively. Above promising results can greatly demonstrate that the proposed method is effective in addressing the over enhancement, the fogging effect and halo phenomenon in existing algorithms, and it can achieve robust illumination adaptive enhancement of wildlife monitoring images.It makes significant contribution to further automatic identification of wildlife monitoring images as well as the improvement of information and automation level in wildlife protection.