Air defense and anti-missile operation usually demands the EO device to quickly detect the sea-skimming missile in a given area. Because the small sea-skimming target usually features low brightness, small size and susceptible to interference from the sea horizon and sea surface clutter. Therefore, a quick search and threat evaluation algorithm is proposed against sea-skimming small targets. First, statistic row mean value and gradient are worked out, and the sea horizon is fitted by means of least square criterion. The background shall be suppressed by using morphological factor and the image shall be binarized by using self-adaptive threshold. Finally, statistic suspicious target features are carried out for threat evaluation of these suspicious targets, sea clutter and sea horizon interference are eliminated and the high priority threat is picked up.
Although various image denoising methods such as PDE-based algorithms have made remarkable progress in the past years, the trade-off between noise reduction and edge preservation is still an interesting and difficult problem in the field of image processing and analysis. A new image denoising algorithm, using a modified PDE model based on pixel similarity, is proposed to deal with the problem. The pixel similarity measures the similarity between two pixels. Then the neighboring consistency of the center pixel can be calculated. Informally, if a pixel is not consistent enough with its surrounding pixels, it can be considered as a noise, but an extremely strong inconsistency suggests an edge. The pixel similarity is a probability measure, its value is between 0 and 1. According to the neighboring consistency of the pixel, a diffusion control factor can be determined by a simple thresholding rule. The factor is combined into the primary partial differential equation as an adjusting factor for controlling the speed of diffusion for different type of pixels. An evaluation of the proposed algorithm on the simulated brain MRI images was carried out. The initial experimental results showed that the new algorithm can smooth the MRI images better while keeping the edges better and achieve higher peak signal to noise ratio (PSNR) comparing with several existing denoising algorithms.