Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

A Bayer-like White-RGB (W-RGB) color filter array (CFA) was invented for overcoming the weaknesses of commonly used RGB based Bayer CFA. In order to reproduce full-color images from the Bayer-like W-RGB CFA, a demosaicing or a CFA interpolation process which estimates missing color channels of raw mosaiced images from CFA is an essential process for single sensor digital cameras having CFA. In the case of Bayer CFA, numerous demosaicing methods which have remarkable performance were already proposed. In order to take advantage of both remarkable performance of demosaicing method for Bayer CFA and the characteristic of high-sensitive Bayer-like W-RGB CFA, a new method of transforming Bayer-like W-RGB to Bayer pattern is required. Therefore, in this letter, we present a new method of transforming Bayer-like W-RGB pattern to Bayer pattern. The proposed method mainly uses the color difference assumption between different channels which can be applied to practical consumer digital cameras.

Most images obtained with imaging sensors contain Bayer patterns and suffer from blurring caused by the lens. In order to convert a blurred Bayer-patterned image into a viewable image, demosaicing and deblurring are needed. These concepts have been major research areas in digital image processing for several decades. Despite their importance, their performance and efficiency are not satisfactory when considered independently. In this paper, we propose a joint deblurring and demosaicing method in which edge direction and edge strength are estimated in the Bayer domain and then edge adaptive deblurring and edge-oriented interpolation are performed simultaneously from the estimated edge information. Experimental results show that the proposed method produces better image quality than conventional algorithms in both objective and subjective terms.

As a basic tool for deriving sparse representation of a color image from its atomic-decomposition with a redundant dictionary, the authors have recently proposed a new kind of shrinkage technique, viz. color shrinkage, which utilizes inter-channel color dependence directly in the three primary color space. Among various schemes of color shrinkage, this paper particularly presents the soft color-shrinkage and the hard color-shrinkage, natural extensions of the classic soft-shrinkage and the classic hard-shrinkage respectively, and shows their advantages over the existing shrinkage approaches where the classic shrinkage techniques are applied after a color transformation such as the opponent color transformation. Moreover, this paper presents the applications of our color-shrinkage schemes to color-image processing in the redundant tight-frame transform domain, and shows their superiority over the existing shrinkage approaches.

This paper introduces a fast image mosaicing technique that does not require costly search on image domain (e.g., pixel-to-pixel correspondence search on the image domain) and the iterative optimization (e.g., gradient-based optimization, iterative optimization, and random optimization) of geometric transformation parameter. The proposed technique is organized in a two-step manner. At both steps, histograms are fully utilized for high computational efficiency. At the first step, a histogram of pixel feature values is utilized to detect pairs of pixels with the same rare feature values as candidates of corresponding pixel pairs. At the second step, a histogram of transformation parameter values is utilized to determine the most reliable transformation parameter value. Experimental results showed that the proposed technique can provide reasonable mosaicing results in most cases with very feasible computations.

This paper presents a color demosaicing method applied to the Bayer pattern color filter array (CFA). Reliable estimation of an edge direction, edge-directed asymmetric interpolation, and the use of color samples at immediate neighbors are considered as the key guidelines for smooth and sharp image restoration. Also, special interest is directed to local areas that are rich in high spatial frequency variations. For suppression of false colors likely to occur in those areas, a hue vector representation is introduced so that the spatial correlation between different color components may be exploited in consistent with the local constant-hue principle. Smoothing is repeated in the hue vector field a few times. Experimental results have shown preferable performances in terms of PSNR, CIELAB color difference, hue angle difference, CIE chromaticity and visual appearance, in particular resulting in less false colors.

This paper presents a color demosaicing method by introducing iterative asymmetric average interpolation. Missing primary colors on a Bayer pattern color filter array (CFA) are estimated by an asymmetric average interpolation where less intensity variation is assumed to be of stronger significance, before sharpness of an initial estimate is further improved by an iterative procedure. The iteration is implemented by an observation process followed by a restoration process. The former is modeled by blurring followed by CFA sampling and the latter is completely as same as the color demosaicing method initially applied. Experimental results have shown a favorable performance in terms of PSNR and visual appearance, in particular, in sharpness recovery.

We describe a theoretically optimal algorithm for computing the homography between two images. First, we derive a theoretical accuracy bound based on a mathematical model of image noise and do simulation to confirm that our renormalization technique effectively attains that bound. Then, we apply our technique to mosaicing of images with small overlaps. By using real images, we show how our algorithm reduces the instability of the image mapping.

Given a set of still images taken from a hand-held camera, we present a fast method for mosaicing them into a single blended picture. We design time- and memory- efficient still image mosaicing algorithms based on geometric point feature matchings that can handle both arbitrary rotations and large zoom factors. We discuss extensions of the methodology to related problems like the recovering of the epipolar geometry for 3d reconstruction and object recognition tasks.