Enhancement of vehicular night vision thermal infrared images is an important problem in intelligent vehicles. We propose to create a colorful three-dimensional (3-D) display of infrared images for the vehicular night vision assistant driving system. We combine the plane parameter Markov random field (PP-MRF) model-based depth estimation with classification-based infrared image colorization to perform colored 3-D reconstruction of vehicular thermal infrared images. We first train the PP-MRF model to learn the relationship between superpixel features and plane parameters. The infrared images are then colorized and we perform superpixel segmentation and feature extraction on the colorized images. The PP-MRF model is used to estimate the superpixel plane parameter and to analyze the structure of the superpixels according to the characteristics of vehicular thermal infrared images. Finally, we estimate the depth of each pixel to perform 3-D reconstruction. Experimental results demonstrate that the proposed method can give a visually pleasing and daytime-like colorful 3-D display from a monochromatic vehicular thermal infrared image, which can help drivers to have a better understanding of the environment.
In the past few years, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Based on LPP, we propose a novel feature extraction method, called uncorrelated locality preserving projection (ULPP). We show that the extracted features via ULPP are statistically uncorrelated, which is desirable for many pattern analysis applications. We compare the proposed ULPP approach with LPP and principal component analysis (PCA) on the publicly available data sets, FERET and AR. Experimental results suggest that the proposed ULPP approach provides a better representation of the data and achieves much higher recognition accuracies.
In this paper, a novel image enhancement technique suitable for infrared image, dualistic sub-image enhancement based on two-dimensional histogram analysis and histogram equalization, is put forward. Firstly, the infrared image is segmented to two sub-images according to the correlation between the neighboring pixels, which is based on the two-dimensional histogram analysis. Then each sub-image is enhanced based on histogram equalization. At last, we get the result after the processed sub-images are composed into one image. The experiment result indicates that the algorithm can not only enhance image information effectively but also keep the fine part of original infrared image well. And this algorithm eliminates the drawback of traditional histogram equalization that the fine part is not easy to control and protect.
In this paper, the measurement and analysis of the second-generation image intensifier have been discussed in detail. On the base of analyzing on the noise measurement principle of the second-generation image intensifier, the variable aperture & dynamic scanning noise measurement technique has been put forward. By using photoelectric multiply-tube which is low noise and high gain as the low light detector, the variable aperture & dynamic scanning noise measurement system of the second-generation image intensifier has been developed. The design and the schematic diagram of this noise measurement system have been present. Based on the above, the SNR has been tested and analyzed with variable incidence illumination, variable aperture in fixed-point measurement and scanning measurement condition. The corresponding noise distribution curves have been drawn. At last the characteristics of this noise measurement system of the second-generation image intensifier have been given out. This noise measurement system has important meaning on the design, evaluating and manufacturing of new photoelectric imaging device.
Low light level (LLL) image communication has received more and more attentions in the night vision field along with the advance of the importance of image communication. LLL image compression technique is the key of LLL image wireless transmission. LLL image, which is different from the common visible light image, has its special characteristics. As still image compression, we propose in this paper a wavelet-based image compression algorithm suitable for LLL image. Because the information in the LLL image is significant, near lossless data compression is required. The LLL image is compressed based on improved EZW (Embedded Zerotree Wavelet) algorithm. We encode the lowest frequency subband data using DPCM (Differential Pulse Code Modulation). All the information in the lowest frequency is kept. Considering the HVS (Human Visual System) characteristics and the LLL image characteristics, we detect the edge contour in the high frequency subband image first using templet and then encode the high frequency subband data using EZW algorithm. And two guiding matrix is set to avoid redundant scanning and replicate encoding of significant wavelet coefficients in the above coding. The experiment results show that the decoded image quality is good and the encoding time is shorter than that of the original EZW algorithm.
In the area of low light level (LLL) night vision, improving the LLL image quality has received more and more attentions. In this paper, we use the instantaneous laser (near infrared) assistant vision technology to obtain the laser assistant vision (LAV) image and realized the fusion of LLL image and LAV image using wavelet transform. The information feature of the two kinds of images is different because the spectrums they respond are different. In the fusion process, the images are first decomposed based on wavelet transform. We construct the multiresolution analysis of the fused image by considering the multiresolution contrast of the source images. The interested image features can be enhanced. At last the fused image is reconstructed by the inverse wavelet transform. The experiment results show that the fused image is better than any of the individual source images and the fusion of the LAV image and the LLL image can improve the image quality of LLL TV. This technology is meaningful for night vision.
The area of low light level (LLL) night vision, improving the LLL image quality by using infrared laser assistant vision technology has been proposed as an important subject. In this paper, we realized the fusion of the instantaneous laser assistant vision image and LLL image in frequency-domain. The features of the two kinds of images are different because the spectrums they respond are different. In frequency-domain, we assign different threshold of the high and the low frequency in order to realize the fusion processing. The details and the contours of the scene are enhanced respectively. The experiment results show that the fusion of instantaneous laser assistant vision infrared image and LLL image can improve the image quality effectively. The fused images and the source images are presented in this paper.