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A new image registration method for multimodal images is proposed in this paper. This method is a combination of the modified scale invariant feature transform (SIFT) feature extraction algorithm and the shape-context feature descriptor. Salient points of multimodal images are extracted by using the modified SIFT feature extraction algorithm. Then each salient point is described by using the shape-context descriptor that formed a feature vector from the orientation histograms of the subregion around each salient point. After salient points matching by using Euclidean distance, random sample consensus algorithm is used to eliminate wrong corresponding pairs. At last, multimodal images registration is achieved by affine transformation and bilinear interpolation. Experimental results for registration of IR images and electro-optical images show that this method has a good registration result.

An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.