Hanlin Qin

Infrared and visible image fusion using multiscale directional nonlocal means filter

Fusion of infrared and visible images is a significant research area in image analysis and computer vision. The purpose of infrared and visible image fusion is to combine the complementary image information of the source images into a fused image. Thus, it is vital to efficiently represent the important image information of the source images and choose rational fusion rules. To achieve this aim, an image fusion method using multiscale directional nonlocal means (MDNLM) filter is proposed in this paper. The MDNLM combines the feature of preserving edge information by the nonlocal means filter with the capacity of capturing directional image information by the directional filter bank, which can effectively represent the intrinsic geometric structure of images. The MDNLM is a multiscale, multidirectional, and shift-invariant image decomposition method, and we use it to fuse infrared and visible images in this paper. First, the MDNLM is discussed and used to decompose the source images into approximation subbands and directional detail subbands. Then, the approximation and directional detail subbands are fused by a local neighborhood gradient weighted fusion rule and a local eighth-order correlation fusion rule, respectively. Finally, the fused image can be obtained through the inverse MDNLM. Comparison experiments have been performed on different image sets, and the results clearly demonstrate that the proposed method is superior to some conventional and recent proposed fusion methods in terms of the visual effects and objective evaluation.

Infrared and visible image fusion with spectral graph wavelet transform.

Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

Visible and infrared image registration based on visual salient features

Abstract. In order to improve the precision of visible and infrared (VIS/IR) image registration, an image registration method based on visual salient (VS) features is presented. First, a VS feature detector based on the modified visual attention model is presented to extract VS points. Because the iterative, within-feature competition method used in visual attention models is time consuming, an alternative fast visual salient (FVS) feature detector is proposed to make VS features more efficient. Then, a descriptor-rearranging (DR) strategy is adopted to describe feature points. This strategy combines information of both IR image and its negative image to overcome the contrast reverse problem between VIS and IR images, making it easier to find the corresponding points on VIS/IR images. Experiments show that both VS and FVS detectors have higher repeatability scores than scale invariant feature transform in the cases of blurring, brightness change, JPEG compression, noise, and viewpoint, except big scale change. The combination of VS detector and DR registration strategy can achieve precise image registration, but it is time-consuming. The combination of FVS detector and DR registration strategy can also reach a good registration of VIS/IR images but in a shorter time.

Multi-focus image fusion using a guided-filter-based difference image

The aim of multi-focus image fusion technology is to integrate different partially focused images into one all-focused image. To realize this goal, a new multi-focus image fusion method based on a guided filter is proposed and an efficient salient feature extraction method is presented in this paper. Furthermore, feature extraction is primarily the main objective of the present work. Based on salient feature extraction, the guided filter is first used to acquire the smoothing image containing the most sharpness regions. To obtain the initial fusion map, we compose a mixed focus measure by combining the variance of image intensities and the energy of the image gradient together. Then, the initial fusion map is further processed by a morphological filter to obtain a good reprocessed fusion map. Lastly, the final fusion map is determined via the reprocessed fusion map and is optimized by a guided filter. Experimental results demonstrate that the proposed method does markedly improve the fusion performance compared to previous fusion methods and can be competitive with or even outperform state-of-the-art fusion methods in terms of both subjective visual effects and objective quality metrics.

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.

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.

Infrared decoys recognition method based on geometrical features

After a decoy is released, it can fly around the target aircraft in a short period of time. And it can radiate infrared spectral radiation similarly to the target do. So it is difficult to recognize the target aircraft. But in infrared images, decoys and targets have different geometrical features. So an infrared decoys recognition method based on the geometrical features is proposed in this paper. The geometrical features of the candidates in each image are extracted, such as the major axis, the minor axis, the aspect ratio, area etc. Then the differences on these geometrical features can be used to recognize targets and decoys. A simulation was done on a set of images that contain decoys and targets by using this method. The results show that the algorithm proposed in this paper can better distinguish infrared decoys and targets.

Image Registration Algorithm Based on Modified GLOH Descriptor for Infrared Images and Electro-Optical Images

A new image registration algorithm for infrared images and electro-optical images is proposed in this paper. This algorithm is a combination of SIFT feature extraction algorithm and GLOH feature descriptor. Salient points of infrared images and electro-optical images are extracted along edges of images by using SIFT feature extraction algorithm. Then each salient point is described by using modified GLOH descriptor that formed a feature vector from the orientation histogram of sub-region around each salient point. After salient points matching by using Euclidean distance, RANSAC algorithm is used to eliminate wrong corresponding pairs. Then registration of infrared images and electro-optical images is achieved by affine transformation and bilinear interpolation. Experimental results for registration of infrared images and electro-optical images show that this algorithm has a good registration result.