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Color Channel Reconstruction for Multi-Color Multi-View Images

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Color Channel Reconstruction for Multi-Color Multi-View Images https://doi.org/10.2352/ISSN.2470-1173.2018.11.IMSE-292 © 2018, Society for Imaging Science and Technology Color Channel Reconstruction for Multi-Color Multi- View Images Using Disparity and Color Similarity-based Local Linear Regression Daniel Kiesel, Thomas Richter, Jürgen Seiler, André Kaup; Chair of Multimedia Communications and Signal Processing; Friedrich- Alexander-Universität Erlangen-Nürnberg, Germany Abstract 1 Most digital cameras today employ Bayer Color Filter Ir Arrays in front of the camera sensor. In order to create a true-color image, a demosaicing step is required intro- 2 ducing image blur and artifacts. Special sensors like the Ig I˜ Iˆ Foveon X3 circumvent the demosaicing challenge by using pixels lying on top of each other. However, they are not Ib commonly used due to high production cost and low flex- 1 ibility. In this work, a multi-color multi-view approach is 1 Registration and Projection presented in order to create true-color images. Therefore, 2 Color Channel Reconstruction the red-filtered left view and the blue-filtered right view are registered and projected onto the green-filtered center view. Figure 1: System overview. Due to the camera offset and slightly different viewing an- gles of the scene, object occlusions might occur for the side channels, hence requiring the reconstruction of missing in- view system in the context of polarization imaging for in- formation. For that, a novel local linear regression method dustrial inspection. By using multiple off-the-shelf compo- is proposed, based on disparity and color similarity. Sim- nents equipped with different polarization filters, a supe- ulation results show that the proposed method outperforms rior image quality has been demonstrated while maintainig existing reconstruction techniques by on average 5 dB. flexibility and avoiding special purpose cameras. In this work, a novel and flexible framework for multi- Introduction color multi-view image acquisition is proposed. For that, Humans are used to perceive the world in color. Con- an array with three standard digital grayscale cameras is sequently, they expect digital images to be recorded and used. In front of each camera, either a red, green, or a blue displayed in color, as well. Typically, Color Filter Ar- color filter is located. The proposed setup provides various rays (CFAs) are employed in front of the camera sensor to benefits. Compared to Bayer filtering, no demosaicing step allow for colored images. Bayer CFAs [1] in particular, de- is required and the full spatial resolution is preserved. In scribe a characteristic pattern of red, green, and blue filter addition, the system is flexible and by far not limited to elements. As each filter element is assigned to one pixel, RGB color imaging. Replacing the color filters in front of for each pixel only one color channel is available. Thus, the cameras allows to easily adapt to other applications, the missing color channels have to be estimated in order to e.g. in the area of multi-spectral imaging. obtain a true-color RGB image. This process is called de- In a first step, the side views are projected onto the mosaicing and introduces image blur and artifacts [2]. Ex- center view. Then, for image regions where not all three tensive research has been conducted in order to find demo- color channels are available after the projection, the miss- saicing techniques that minimize the quality degredation ing color information is reconstructed exploiting the local to fulfill the increasing requirements for image quality [3], neighborhood and color channel correlations. [4]. Alternatively, special sensors like the Foveon X3 have The rest of the paper is structured as follows. After been developed, circumventing the demosaicing step [3]. explaining the overall system in the next section, the paper But, those sensors are expensive and not widely spread in focuses on the registration and the proposed color channel the consumer market. Regardless of using standard Bayer reconstruction method. Then, simulation results are given. patterns or special sensors, the spectral sensitivity is fixed, The paper finally concludes with the last section. limiting the number of potential applications. In contrast, in multi-spectral imaging, the required System Overview spectral sensitivity highly depends on the chosen applica- The proposed setup can be seen in Figure 1. Three tion [5], [6]. For that, filter wheel based systems [7] could cameras are positioned next to each other and in front of increase flexibility but are restricted to static scenes. each camera a different filter, corresponding to one of the Recently, the authors in [8] proposed to use a multi- three primary colors, is mounted. As a result, three images IS&T International Symposium on Electronic Imaging 2018 292-1 Image Sensors and Imaging Systems 2018 Ir, Ig, and Ib can be obtained, each containing all pixels for the corresponding red, green, and blue color channel, I3 respectively. The main objective is to project the left image I2 containing the red channel and the right image containing the blue channel onto the center view in order to create the RGB image I˜. The green channel is chosen as the center I1 view, as it contains the most luminance information and because green wavelengths are between blue and red wave- lengths. As the cameras cannot be placed in exactly the same position, each camera has a slightly different view- Figure 2: Illustration of the pixel sets I1, I2, and I3. point entailing two challenges. First, image registration is necessary to find corresponding pixels in the different views before projecting them. This task is challenging because it neighborhood. The result of the cost computation is a ma- does not resemble a global transformation problem but a trix comprising the costs for all possible disparities within local one, as objects closer to the camera are subject to big- a pre-defined search range for every pixel. Now, the cost ger translation than background objects. After performing aggregation step aims at getting more reliable and robust the projection step, the resulting image I˜ comprises pixels costs by smoothing the cost matrix while not aggregating with either one, two, or three color channels. The center across object boundaries. In this work, a cross-based ag- view, corresponding to the green channel, is always avail- gregation method as discussed in [10], [11] is used. In the able. However, depending on the scene geometry, projected disparity computation step, simply the best match is cho- pixels from the side views might be occluded in the cen- sen from the aggregated cost matrix. Finally, a cross-check ter view. Thus, secondly, in order to obtain the true-color and median filtering is used in order to further refine the RGB image Iˆ, the occluded areas have to be reconstructed disparity map. using a proper color channel reconstruction technique. Color Channel Reconstruction Image registration After applying the disparity map for image projection, In general, image registration is the task of aligning three different kinds of pixels exist in the image I˜. First, different images of the same scene. For rectified multi-view there are pixels where all three color channels are present images, the registration can be achieved in terms of dis- after the projection. This set of pixels does not require any parity estimation. In case of rotations or two-dimensional kind of reconstruction thereafter and is called I3. Second, translations, image rectification has to be applied first. Ac- for pixels with two existing color channels I2, the remain- cording to [9], the disparity estimation chain consists of ing third one has to be reconstructed. Lastly, for pixels the sub-tasks matching cost computation, cost aggrega- I1 where only one color channel, i.e., the green channel, tion, disparity computation, and disparity refinement. is present, the missing two color channels have to be re- For cost computation, different matching metrics can constructed. For a sample projection result, Figure 2 illus- be applied. On the one hand, there are intensity-based trates the pixel sets I1, I2, and I3. In order to use the metrics, such as the sum-of-absolute differences or the sum- maximum amount of information, the set I2 is considered of-squared differences. However, since this work deals with first. Subsequently, those results can be used to improve the registration of different color components, intensity- the reconstruction results for I1. based metrics cannot be used. On the other hand, there In literature, various approaches have been already are structure-based metrics, trying to search for point cor- developed. In general, interpolation or image inpainting respondences by comparing the structure of their surround- approaches can be used in order to reconstruct the missing ing blocks. In this work, the zero-mean normalized cross information. The Frequency Selective Extrapolation (FSE) corelation (ZNCC) is used for cost computation. In the fol- is a block-based iterative method for reconstructing lost lowing, the calculation is described for matching between areas in images [12]. It has been successfully applied in the red and the green channel. Note that the calculation a wide range of applications, ranging from difference im- for the blue and the green channel is equivalent. For a age extrapolation [13], over image resampling [14], to the pixel position p and a disparity level d, the ZNCC value reconstruction of synthesized high-frequency content [15]. ZNCC(p,d) is defined according to The basic assumption is that image signals can be repre- sented sparsely in the frequency domain. For the unknown ZNCC(p,d) = pixels within each block, FSE generates a model based on P Ir(q) − I¯r(p) Ig(q − (d,0)) − I¯g(p − (d,0)) the available pixels in the support area as superposition of q∈Np 2-D Fourier basis functions.
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