Rui Lai

Solution for the nonuniformity correction of infrared focal plane arrays

Based on the S-curve model of the detector response of infrared focal plan arrays (IRFPAs), an improved two-point correction algorithm is presented. The algorithm first transforms the nonlinear image data into linear data and then uses the normal two-point algorithm to correct the linear data. The algorithm can effectively overcome the influence of nonlinearity of the detectors response, and it enlarges the correction precision and the dynamic range of the response. A real-time imaging-signal-processing system for IRFPAs that is based on a digital signal processor and field-programmable gate arrays is also presented. The nonuniformity correction capability of the presented solution is validated by experimental imaging procedures of a 128 x 128 pixel IRFPA camera prototype.

Improved non-local means filtering algorithm for image denoising

A novel non-local means (NLM) filtering based image denoising method is proposed. This method is intended to promote the precision of denoising by introducing an optimized weight kernel of NLM filter and an improved neighborhood pre-classification strategy. Results on standard test image show that the proposed method is very successful in noise suppression and detail preserving.

Shearlet transform based anomaly detection for hyperspectral image

Hyperspectral image (HI) contains data in hundreds of narrow contiguous spectral bands, thus it provides a powerful means to distinguish different materials on the basis of their unique spectral signatures. Anomaly detection (AD) is one key part of its application. The shearlet transform (ST) is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks, which can effectively captures smooth contours that are the dominant feature in natural image. In this paper, ST is used in AD for the HI. Firstly, the raw HI data is decomposed into several directional subband at multiple-scale via ST. Thus, the background signal would be reduced in each subband. Secondly, the fourth partial differential equation method is adopted to modify the coefficient of each sub-band, which is for background suppression and anomaly signal enhancement. Thirdly, the kernel-based RX algorithm is adopted to detect the anomaly in each sub-band. Finally, the anomaly signal image is achieved by reconstructing the image with all modified sub-band. Several experiments with a HYDICE data are fulfilled to validate the performance of the proposed method. Compared with the original RX algorithm, experimental results show that the proposed algorithm has better detection performance and lower false alarm probability.

A new image registration method for infrared images and visible images

A new image registration method for infrared images and visible images is proposed in this paper. This method is a modified version of SIFT algorithm. Salient points of infrared images and visible images are extracted along edges of images. Each salient point is assigned a dominant direction which is the peak of orientation histogram of local image around it and a feature vector which is formed from the orientation histograms of sub-region around it. After salient points matching, RANSAC algorithm is used to eliminate wrong corresponding pairs. Experimental results show that this method has a good registration result for infrared images and visible images.

Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays

Nonuniformity is a key factor that influences the performance of the infrared focal plane arrays (IRFPA) imaging system. A nonuniformity correction algorithm based on discrete sequence wavelet transform for IRFPA is presented. The infrared image sequences of detectors are decomposed by the discrete sequence wavelet transform. And then correction coefficients of the offset and the gain for NUC are achieved by calculating the corresponding statistics of the decomposed signal. Finally the NUC is fulfilled. The capability of the algorithm to adaptively correct nonuniformity in IRFPA imagery is demonstrated by using real infrared image sequences with different wavelet function. The algorithm achieves good nonuniformity correction ability.

Adaptive filtering for nonuniformity correction in infrared focal plane arrays

An scene-based nonuniformity correction (NUC) method for infrared focal plane arrays (IRFPA) is presented. The method is based on an optimal recursive estimation based on a fast form of the adaptive filter. The infrared image sequences are input into an adaptive Alter. After certain times recursion calculations are executed frame by frame, the optimal coefficients of the gain and the offset of detector in IRFPA are achieved. Then the NUC is fulfilled ultimately. The algorithm is adaptive to the response drift and achieves higher NUC precision, which are validated by using real infrared image sequences.

A new non-uniformity correction algorithm for IRFPA based on statistical properties of scene

Influenced by detectors’ material, related manufacturing technology etc, every detection element’s responsivity in infrared focal plane arrays(IRFPA) is different, which results in non-uniformity of IRFPA. So non-uniformity correction(NUC) is an important technique for IRFPA. The classical two-point NUC algorithm based on reference sources is analyzed in this paper. And a new NUC algorithm based on statistical characteristics of image serial is presented. In this algorithm, the reference images are constructed from image serial, and correction parameters are computed by using the constructed reference images. Then two-point NUC is applied to output images of IRFPA. Experimental results show that the algorithm proposed in this paper is effective and implemented easily.

Temporal high-pass filter nonuniformity correction algorithm based on guided filter for IRFPA

In this paper, a new temporal high-pass filter nonuniformity correction algorithm based on guided filter is proposed, which address the ghosting artifacts and preserve image details of original image. In this algorithm, the original input image is separated into two parts, which are the high spatial-frequency part that contains most of the nonuniformity and the low spatial-frequency part with well preserved details. Then the fixed pattern noise is estimated from the high spatial-frequency part and subtracted from the original image, which achieves the nonuniformity correction. The performance of this presented algorithm is tested with two infrared image sequences, and the experimental results show that the proposed algorithm can significantly reduce the ghosting artifacts and achieve a better nonuniformity correction performance.

Infrared target detection based on surfacelet transform and total variation

A dim and small target detection method based on surfacelet transform is proposed to improve the performance of dim and small target detection under the complex clouds background. Firstly, the original infrared image is decomposed by the surfacelet transform to extract and analyze the multi-scale and multi-directional characteristics of the image. Then, the total variation and the local mean removal method are utilized to process the high-frequency and the low-frequency sub-bands respectively, which refines the coefficient value of the decomposed sub-bands. Finally, the refined sub-bands are recostructed to make the dim and small target separate from the background clutter signal, and then the background suppression is achieved and the real target is detected effectively. Theoretical analysis and experimental results show that, compared with the wavelet transform method and the total variation method, values of ISCR and BSF of the experimental result by the proposed method is higher, and the result by the proposed method has better effect both in subjective vision and the objective numerical evaluation.

Accuracy estimation of images registration method

Accuracy estimation of image registration is important for image fusion, computer vision and so on image processing tasks. It is a substantial part of image registration process. Without quantitative accuracy estimation, no registration algorithm can be applied pragmatically. In this paper, the parameters to measure accuracy of image registration and their characteristics are introduced systematically. And Cross Validation is used to estimate the parameters of measuring accuracy of image registration. And it is used to evaluate accuracy of SIFT algorithm and manual image registration algorithm. The experimental results show the effectiveness of Cross Validation method.