In order to enhance the restoration quality of Wiener filter, and widen the range of its application, an improvement is made on its basic model, then discuss how adaptive Wiener filter works on motion images, which is based on detecting blur’s direction and depth, and on recursive iterations. As for the process of motion-blurred image of the fast-moving object, experiment indicates an ideal effect can be achieved by this method.
Polarization imaging is another photoelectric imaging detection technology. It has obvious technical advantages in revealing camouflage, penetrating haze, and getting target details. It can gain multiple polarization features images and achieve target detection and recognition through specific polarization information analysis methods such as synthesis and fusion. Because there is a mis-match problem between the polarization features images, polarization image registration performs first. However, existing methods such as mutual information registration and related registration methods are hard to solve the problem of mis-match because of distortion of the polarization imaging lens. In this paper, we present a matching optimization SIFT polarization image registration algorithm found on the standard SIFT registration algorithm. In the sub-matching description, a reversed matching is added, that is, matching in both directions performs to form a symmetrical matching. In the matching set of positive and negative directions, matched feature points pairs satisfying both sets extract. The pair of matching points are only when the pair of feature points are the best matching points. This increases the matching accuracy of feature points and reduces the mismatching rate of descriptions. At the same time, numbers of feature points add in the algorithm using the gray leveling method. Registration experimental results show the registration accuracy of this method is better than the mutual information registration method.
In the target detection process of polarization optics imaging, due to the turbulent effect of the target signal transmitted in the atmosphere and the photoelectric conversion of optical imaging sensors and other factors, Salt-and-Pepper noise which affects the detection accuracy. According to the statistical characteristics of the Salt-and-Pepper noise probability density, a new structure preserved polarization image Salt-and-Pepper noise removal method is proposed. With the new signal sparse representation theory and image inpainting method, only the noise regions is restored by the noise point detecting. In the inpainting process, the structural similarity is considered which can improve the structural information retention ability of polarization image. Numerical simulation results demonstrate the validity of the proposed method both subjectively and objectively.
The different polarization phase angle (orientation) low-resolution images of the same scene have much redundant and complementary information which can be used to construct a high-resolution image. In this paper, we propose a super-resolution (SR) algorithm via sparse and redundant representation with considering the non-local self-similarity in different polarization orientation images. As the redundant over-complete dictionary has many irrelevant atoms which not only reduce the computational efficiency in sparse coding but also reduce the representation accuracy, we learn a local dictionary by applying the principal component analysis (PCA) technique. For an image patch to be coded, the best fitted sub-dictionary is adaptively selected by an adaptive sparse domain selection strategy. To improve the stability and accuracy of sparse coding, the centralized sparse coding algorithm is used. The extensive experimental results demonstrated that the proposed method can effectively reconstruct the polarization image with edge structure preserved and detailed information obtained in terms of PSNR, SSIM and visual perception.
Proc. SPIE. 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
KEYWORDS: Associative arrays, Principal component analysis, Image denoising, Denoising, Global system for mobile communications, Visualization, Image processing, Inverse problems, Image compression, Image segmentation
To get better denoising results, the prior knowledge of nature images should be taken into account to regularize the ill-posed inverse problem. In this paper, we propose an image denoising algorithm via non-local similar neighbor embedding in sparse domain. Firstly, a local statistical feature, namely histograms of oriented gradients of image patches is used to perform the clustering, and then the whole training data set is partitioned into a set of subsets which have similar local geometric structures and the centroid of each subset is also obtained. Secondly, we apply the principal component analysis (PCA) to learn the compact sub-dictionary for each cluster. Next, through sparse coding over the sub-dictionary and neighborhood selecting, the image patch to be synthesized can be approximated by its top <i>k</i> neighbors. The extensive experimental results validate the effective of the proposed method both in PSNR and visual perception.
In this paper we analyse the polarization imaging theory and the commonly process of the polarization imaging detection. Based on this, we summarize our many years’ research work especially in the mechanism, technology and system of the polarization imaging detection technology. Combined with the up-to-date development at home and abroad, this paper discusses many theory and technological problems of polarization imaging detection in detail from the view of the object polarization characteristics, key problem and key technology of polarization imaging detection, polarization imaging detection system and application, etc. The theory and technological problems include object all direction polarization characteristic retrieving, the optical electronic machinery integration designing of the polarization imaging detection system, the high precision polarization information analysis and the polarization image fast processing. Moreover, we point out the possible application direction of the polarization imaging detection technology both in martial and civilian fields. We also summarize the possible future development trend of the polarization imaging detection technology in the field of high spectrum polarization imaging. This paper can provide evident reference and guidance to promote the research and development of the polarization imaging detection technology.
Polarization imaging provides abundant information of object, i.e. surface roughness, texture, physical and chemical characters. Independently, intensity and polarimetric features give incomplete representations of an object of interest. These representations are complementary, and it is expected that the combination of complementary information will reduce false alarms, improve confidence in target identification, and improve the quality of the scene description. Polarization parameter images include the degree of polarization, the angle of polarization, azimuth angle etc. There are not only strong correlations between polarization parameter images, but also different characters, which gives image fusion challenges, namely, how to find the optimal polarization parameter image to take part in image fusion with intensity image. This paper presents a polarization image fusion method based on choquet fuzzy integral. Using this algorithm the best polarization parameter image and intensity image are fused, and the fusion result is evaluated. The experiments show that this method could automatically select the best polarization parameter images from multi-polarization parameters image, the resulting images can yield more detail and higher contrast, and can reduce the noise effectively. It is conducive to the subsequent target detection.
In this paper we propose a polarization image fast fusion approach based on online dictionary learning for sparse non-negative matrix factorization, aiming at improving the efficiency of fusion methods for polarization image based on non-negative matrix factorization. Firstly, all of the polarization parameter images are taken as source data sets for sparse non-negative matrix factorization using online dictionary learning algorithm, so as to extract three feature basis images. Then, after histogram matching, the three feature basis images are mapped into three color channels of IHS color space. Finally, the fused image is achieved via the transform from IHS to RGB color model. Experimental results show that, the proposed method not only has better capacity of color representation capability and effectively pop out detailed information of objects but enhances the running efficiency evidently as well.