In order to solve the problem of ghost artifacts in the traditional nonuniformity correction(NUC) method, a new scene-based guided bilateral filter(GBF) nonuniformity correction was proposed. In this paper, the original input image sequences are processed by the guided bilateral filter firstly, then the expected output imagine with the boundary information was estimated recursively only by using high spatial-frequency part of the image which contains most of the noise and nonuinformity. The method was verified with several infrared image sequences, and several experimental results show that the proposed method can significantly reduce the ghosting artifacts in temporal high-pass filter(THPF) and achieve a better nonunifotmity correction effect.
In hyperspectral images, anomaly detection without prior information develops rapidly. Most of the existing methods are based on restrictive assumptions of the background distribution. However, the complexity of the environment makes it hard to meet the assumptions, and it is difficult for a pre-set data model to adapt to a variety of environments. To solve the problem, this paper proposes an anomaly detection method on the foundation of machine learning and graph theory. First, the attributes of vertexes in the graph are set by the reconstruct errors. And then, robust background endmember dictionary and abundance matrix are received by structured sparse representation algorithm. Second, the Euler distances between pixels in lower-dimension are regarded as edge weights in the graph, after the analysis of the low dimensional manifold structure among the hyperspectral data, which is in virtue of manifold learning method. Finally, anomaly pixels are picked up by both vertex attributes and edge weights. The proposed method has higher probability of detection and lower probability of false alarm, which is verified by experiments on real images.
In traditional scene-based non-uniformity correction methods, ghosting artifacts and image blurring affect the response uniformity of the infrared focal plane array imaging system seriously and decrease the image quality. In order to suppress artifacts ghosting and improve image quality, this paper proposed a new based on kernel regression nonuniformity correction method for infrared image, because of its powerful ability to estimating. The main purpose of proposed method is to obtain reliable estimations of gain and offset parameters. Firstly, in order to suppress the ghost artifacts normally introduced by the strong edge effectively, this paper employs the kernel regression method to estimate the desired pixel value of each detector uint. Then the two correction parameters are achieved with the steepest descent method for the purpose of updating these two parameters synchronously. Finally, more accurate estimations of the two correction parameters can be obtained. Several simulated infrared image sequences are utilized to verify the performance of the proposed method. The results show that our method performs better than other compared methods.