In order to build a robust background model and improve the accuracy of the foreground object detection, we give a comprehensive consideration on the same location pixels of the relevance of time and the correlation of space with its adjacent pixels; and based on the classic ViBe of random algorithm ideas, a kind of complex background model and foreground detection method is proposed. Using the first n series of images to initialize the background model with the sample consistency principle, we can avoid the appearance of the “Ghost” phenomenon; and get the difference between each pixel and its multiple sample value in the background model, and then compute the sum and the average. The average shows the dynamic degree of the background point which is the corresponding pixel background of dynamic feedback information. We get the adaptive clustering threshold and adaptive updating threshold with the dynamic feedback to make random clusters realize the adaptability to dynamic background and combine the global disturbance threshold with the local pixel level judgment threshold to implement the immunity of illumination with slow changes, fast changes or sudden changes, so that we can segment the prospect target accurately. By selecting neighborhood pixels to update the neighborhood background randomly in terms of spatial information dissemination mechanism, a good detection effect is obtained in the case of camera shake. Through multiple sets of test data, experimental results show that this algorithm can significantly improve the adaptability and robustness of the background model such as dynamic backgrounds, illumination changes, and camera shake. The algorithm can well apply to the occasion of moving targets in infrared image detection, and expand its application range. Without any image preprocessing and morphological post-processing, the original detection accuracy of foreground is superior to other algorithms.

According to irradiation-reflection model, by combining the generalized bounded operation model with guide filter, the problem of enhancement for multispectral degraded images with blurred details can be effectively solved and the contrast and low signal-to-noise ratio can be improved. The multi-scale reflection component image, i.e., final enhanced image is obtained through the following procedures: using the adaptive different scales of guide filter function as surround function estimate reaction; separating out the high-low-frequency information; obtaining the different scales Irradiation images which react the overall structure of the image; using the bounded generalized logarithmic ratio (GLR) model addition to replace the Retinex logarithmic transformation; taking a similar logarithmic transformation to the original image to improve the contrast of the image and make the dark area of image details enhanced; again using GLR model subtraction to remove illuminate components from the original image to segment the different scales of the reflection image, thereby avoiding the loss of small details and the big details caused halo effect and noise interference. With four direction Sobel gradient image which reflects the comprehensive edge details of image information the adaptive gain function can be obtained. To avoid the smooth area noise amplification, by using the GLR model multiplication and addition to fuse the effective information of different scales images, the multi-scale reflection image, namely the final enhanced image are obtained. The effective suppression of the emergence of halo effect and computing overflow, which can retain a large number of image details; the comparision of subjective visual effect and the quantitative parameter analysis of the visible low illumination image, haze image, infrared image and X-ray medical images (a total of four groups of multispectral degraded images), the use of the contrast and entropy as evaluation indices, qualitative and quantitative comparison with a variety of image enhancement algorithms, show that the proposed algorithm strengthens and keeps the details of the image texture and edge, realizes the image contrast enhancement and the effective dynamic range compression, has a strong anti-noise ability, and can meet a variety of practical engineering image enhancement needs. The results of the study has been used in the infrared thermal imager, and good results have been achieved. The proposed algorithm is only for 8-bit grayscale image enhancement, and the color image enhancement will be studied in the future.