Infrared and visible image fusion methods and applications: A survey

A review of multimodal image matching: Methods and applications

Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs

The visibility of outdoor images is greatly degraded due to the presence of fog, haze, smog etc The poor visibility may cause the failure of computer vision applications such as intelligent transportation systems, surveillance systems, object tracking systems, etc To resolve this problem, many image dehazing techniques have been developed These techniques play an important role in improving performance of various computer vision applications Due to this, the researchers are attracted toward the dehazing techniques This paper carries out a comprehensive review of dehazing techniques to show that these could be effectively applied in real-life practice On the other hand, it encourages the researchers to use these techniques for removal of haze from hazy images The seven main classes of dehazing technique, such as depth estimation, wavelet, enhancement, filtering, supervised learning, fusion, meta-heuristic techniques and variational model are addressed In addition, this paper focuses on mathematical models of dehazing techniques along with their implementation aspects Finally, some considerations about challenges and future scope in dehazing techniques are discussed

A Comprehensive Review of Computational Dehazing Techniques

This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can be converted to simple scale transformation and translation transformation between two sensor images under long-distance and parallel imaging conditions. Second, with the introduction of metadata, a scale calculation algorithm is designed according to spatial geometry, and a coarse translation estimation algorithm is presented based on coordinate transformation. Third, the problem of non-strictly aligned edges in precise translation estimation is solved via edge–distance field transformation. A searching algorithm based on particle swarm optimization is introduced to improve efficiency. Additionally, a new image fusion algorithm is designed based on a pulse coupled neural network and nonsubsampled contourlet transform to meet the special requirements of preserving color information, adding infrared brightness information, improving spatial resolution, and highlighting target areas for unmanned aerial vehicle (UAV) applications. A medium-altitude UAV is employed to collect datasets. The result is promising, especially in applications that involve other medium-altitude or high-altitude UAVs with similar system structures.

Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing

Dim and small infrared target fast detection guided by visual saliency

The invention discloses a characteristic matching and MeanShift algorithm-based target tracking method. The method comprises the following steps: inputting an image sequence, carrying out background reconstruction on the image sequence, obtaining a target area of an initial moment and modelling by adopting a MeanShift algorithm; carrying out SIFT characteristic extraction on a target area model of the initial moment and taking the SIFT characteristic point of the target area model of the initial moment as an initial characteristic point of a characteristic library; calculating the initial position, size parameter and rotation parameters of the current frame of target through SIFT characteristic matching; accurately positioning the target by adopting the MeanShift algorithm; calculating a shielding factor of the target, judging the shielding degree of the target and determining the tracking mode of the target; and ending the target tracking after all the images in the image sequence are tracked. According to the method, the MeanShift algorithm and the SIFT characteristic matching algorithm are combined and the advantages of the two algorithms are exploited so that the stable tracking the target is realized.

Characteristic matching and MeanShift algorithm-based target tracking method

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.

Visible and infrared image registration based on visual salient features

The invention discloses an infrared image and visible image registration method based on a visual attention mechanism. The infrared image and visible image registration method based on the visual attention mechanism comprises the following steps of inputting an original infrared image and an original visible image, and obtaining a visual salience region of the infrared image and a visual salience region of the visible image through a visual attention mechanism model; carrying out rough matching on the visual salience region of the infrared image and the visual salience region of the visible image through invariant moment calculation of the Hu moment; searching for the center of mass in the visual salience region of the infrared image and the center of mass in the visual salience region of the visible image after rough matching is accomplished, using the centers of mass as visual salience points, and using the visual salience points to represent the visual salience regions; carrying out fine matching on the visual salience points on the basis of the de-mean normalization cross-correlation principle; purifying accurately-matched images through an RANSAC algorithm, calculating a registration estimation function, carrying out image transformation through the registration estimation function to achieve registration and output of the images. According to the infrared image and visible image registration method based on the visual attention mechanism, due to the fact that the visual attention mechanism of human eyes is simulated, serves as an image characteristic detection method, and is used for extracting stable characteristics, after matching is carried out, registration between the infrared image and the visible image is accurately achieved.

Infrared image and visible image registration method based on visual attention

An image registration method based on remarkable visual features comprises the steps of inputting a reference image and an image to be registered; extracting a remarkable visual feature point set of the reference image and a remarkable visual feature point set of the image to be registered; calculating the directions of all remarkable visual feature points of the reference image and all remarkable visual feature points of the image to be registered and description vectors of the feature points according to the directions of the remarkable visual feature points to obtain a feature description vector set of the reference image and a feature description vector set of the image to be registered; calculating distances between the feature description vectors of the reference image and the feature description vectors of the image to be registered and conducting matching on the feature points of the reference image and the feature points of the image to be registered; removing mistaken matching pairs, obtaining correct matching pairs in the reference image and the image to be registered, calculating an affine transformation matrix between the reference image and the image to be registered according to the coordinates of the correct matching pairs between the two images and conducting corresponding transformation on the image to be registered according to the affine transformation matrix so as to achieve image registration.

Xiang Yi

Papers

Dim and small infrared target fast detection guided by visual saliency

Characteristic matching and MeanShift algorithm-based target tracking method

Visible and infrared image registration based on visual salient features

Infrared image and visible image registration method based on visual attention

Image registration method based on remarkable visual features