Wavefront coding, a technique of optical-digital hybrid image, can be used to extend the depth of the field. However, it sacrifices the signal-to-noise ratio (SNR) of system at a certain degree, especially on focus situation. The on-focus modulation transfer function (MTF) of wavefront coding system is much lower than that of generally traditional optical system. And the noise will be amplified in the digital image processing. This paper analyzes characteristics of the SNR of the wavefront coding system in the frequency domain and calculates the rate of noise amplification in the digital processing. It also explains the influence of the image detector noise severely reducing the restored quality of images. In order to reduce noise amplification in the process of image restoration, we propose a modified wiener filter which is more suitable for restoration in consideration of noise suppression. The simulation experiment demonstrates that the modified wiener filter, compared with traditional wiener filter, has much better performance for wavefront coding system and the restored images having much higher SNR in the whole depth of the field.
As an important branch of infrared imaging technology, infrared target tracking and detection has a very important scientific value and a wide range of applications in both military and civilian areas. For the infrared image which is characterized by low SNR and serious disturbance of background noise, an innovative and effective target detection algorithm is proposed in this paper, according to the correlation of moving target frame-to-frame and the irrelevance of noise in sequential images based on OpenCV. Firstly, since the temporal differencing and background subtraction are very complementary, we use a combined detection method of frame difference and background subtraction which is based on adaptive background updating. Results indicate that it is simple and can extract the foreground moving target from the video sequence stably. For the background updating mechanism continuously updating each pixel, we can detect the infrared moving target more accurately. It paves the way for eventually realizing real-time infrared target detection and tracking, when transplanting the algorithms on OpenCV to the DSP platform. Afterwards, we use the optimal thresholding arithmetic to segment image. It transforms the gray images to black-white images in order to provide a better condition for the image sequences detection. Finally, according to the relevance of moving objects between different frames and mathematical morphology processing, we can eliminate noise, decrease the area, and smooth region boundaries. Experimental results proves that our algorithm precisely achieve the purpose of rapid detection of small infrared target.