Lai Rui

Image registration method for multimodal images

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.

Nonuniformity correction for an infrared focal plane array based on diamond search block matching

In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.

Infrared Complex Background Suppression Based on Vision Perception Model

To improve the detection performance for weak and small targets signal in complex infrared background, such as the ground and the cloud, the small target background suppression algorithm based on vision perception model (VPM) is presented. Firstly, with simple cell receptive field model, original infrared image is decomposed to two images by different Gabor functions using convolution. And then, the nonlinear convergence function of complex cell response is utilized to fusion two images obtained by separation small target with background clutter in infrared image. Finally, the target image is obtained by using classical adaptive threshold method. Several groups of experimental results demonstrate that the proposed method can suppress the infrared background effectively, compared with several classical infrared dim and small target background suppression methods, such as local means remove and two-dimensional least means square filter methods.

Total variation regularized back-projection method for spatial resolution enhancement: TOTAL VARIATION REGULARIZED BACK-PROJECTION METHOD

Iterative back-projection (IBP) technique is commonly utilized to minimize the error of the super resolution (SR) reconstruction and acquire a high spatial resolution image with appealing visual effect. However, the IBP-based SR method suffers from the chessboard and ringing effect around edges. In this paper, we employ the total variation as a part of the penalty function to minimize the reconstruction error, which helps to avoid across-edge smoothing and suppress the above-mentioned artifacts simultaneously. The experimental results validate that the proposed method leads to significant gain in terms of both the peak signal-to-noise ratio and the structural similarity index, and promotes the visual effect of the image considerably.

Total variation regularized back‐projection method for spatial resolution enhancement

Iterative back-projection (IBP) technique is commonly utilized to minimize the error of the super resolution (SR) reconstruction and acquire a high spatial resolution image with appealing visual effect. However, the IBP-based SR method suffers from the chessboard and ringing effect around edges. In this paper, we employ the total variation as a part of the penalty function to minimize the reconstruction error, which helps to avoid across-edge smoothing and suppress the above-mentioned artifacts simultaneously. The experimental results validate that the proposed method leads to significant gain in terms of both the peak signal-to-noise ratio and the structural similarity index, and promotes the visual effect of the image considerably.

Anomaly Detection Algorithm Based on Nonsubsampled Pyramid Decomposition and Kernel Unsharp Masking for Hyperspectral Image

An anomaly detection algorithm for hyperspectral images based on nonsubsampled Pyramid decomposition (NSPD) was proposed. Both spatial and spectral information have been used to locate and detect the anomaly under the condition of no prior knowledge about the anomaly and the background. Firstly, the hyper-spectral images was decomposed into a series of different scale sub-bands using NSPD; and then using the correlation of neighborhood coefficient of different scale space in a wave-band, the background data was optimally predicted by reducing the anomalous data using the improved kernel unsharp masking filter in different scale of each sub-band. Finally the anomaly targets could be detected by using the RX operator in the feature space. Numerical experiments were conducted on real and synthesized hyperspectral data to validate the effectiveness of the proposed algorithm. Compared with the classical RX algorithm, several experimental results show that the proposed algorithm has better detection performance and lower false alarm probability.