Research On Image Processing Algorithms Of High-resolution Infrared Imaging

Infrared imaging technique has achieved much progress and been applied to military and civilian fields in recent years, such as target tracking, imaging guidance, environmental monitoring and so on. Due to the structure of detector, manufacture technology, material etc, there are some problems in infrared detector, such as nonuniformity noise and undersampling, which result in low temperature resolution and spatial resolution, bad imaging quality and short detecting distance. High resolution imaging, which is theoretically important as well as practically urgent, is an active topic in infrared imaging region during recent years. For improving temperature resolution and spatial resolution of infrared imaging, nonunifomity correction and superresolution of infrared imaging are mainly discussed. The studies are carried out in the following aspects:1) Nonuniformity correction technique of infrared focal plane array (IRFPA) detector. The mechanism of nonuniformity was discussed from IRFPA, readout circuit and environmental temperature. The exponential relationship between detector response and environmental temperature was established. For solving the nonuniformity drift in time, three novel sence-based NUC algorithms were proposed. Aiming at the time and spatial features of signal output, a nonuniformity correction algorithm based on iterative theory and blackbody calibration was presented. The fixed stripe noise was estimated using Gaussian distribution of the difference of adjacent readout channels. New expectation estimation was constructed and the step value was adjusted adaptively based on the LMS theory in the improved neural network nonuniformity correction. A ghosting eliminated technique was proposed based on high-pass in time domain and low-pass in spatial domain. Accordding to constant-statistics theory, current pixel whether was used for computing offset parameter or not depending on the existed offset estimation.2) Microscan superresolution imaging technique. We discussed the image processing of microscan including pre-filtering and interpolate construction. In the pre-filtering part, aiming at ill-conditioned problem of the P-M equation, an improved anisotropic diffusion algorithm was presented. Based on pixel gradient and local variance characteristics, a diffusion coefficient was established which satisfies the condition of diffusion coefficient presented by Charbonnier. In the interpolation reconstruction part, an edge-preserved interpolation and an improved Lagrange interpolation algorithm were proposed, respectively. According to local root-mean-square edge model, the image was divided into smoothness and edge regions. The interpolation was carried out by using adjacent pixels in the smoothness region and the pixel of greatest gradient direction in the edge region, respectively. Improved Lagrange interpolation algorithm divided image into multi-isotropy regions, the exponent number of Lagrange equation was adjusted self-adaptively in every isotropy region.3) Multiframe superresolution reconstruction. Our discussion focuses on image registration and reconstruction. In the image registration part, a multi-scale templates registration algorithm was proposed based on morphological and Mallat multi-resolution analysis theories, which can effectively eliminate the noise. In the image reconstruction part, from Bayes random field angle, a self-adaptive threshold MAP superresolution reconstruction was proposed based on Huber-Markov model of regularization.Experimental tests and simulations show the proposed algorithms are superior compared with existed algorithms. These new technologies have been applied to the imported cool and uncool staring infrared imaging system and microscan imaging system, which achieve promising results. All of these show these key techniques have real value for improving the quality of infrared image.