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Multiscale Gradient Based – Directional CFA Interpolation with Refinement

Aug 08, 2015

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  1. 1. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 1 APRIL 2015 - ISSN: 2349 - 9303 90 Multiscale Gradient Based Directional CFA Interpolation with Refinement Aarthy Poornila.A1 1 Mepco Schlenk Engineering College, ECE Department [email protected] R. Mercy Kingsta2 Assistant Professor 3 Mepco Schlenk Engineering College, ECE Department [email protected] AbstractSingle sensor digital cameras capture only one color value for every pixel location. The process of reconstructing a full color image from these incomplete color samples output from an image sensor overlaid with a color filter array (CFA) is called demosaicing or Color Filter Array (CFA) interpolation. The most commonly used CFA configuration is the Bayer filter. The proposed demosaicing method makes use of multiscale color gradients to adaptively combine color difference estimates from horizontal and vertical directions and determine the contribution of each direction to the green channel interpolation. This method does not require any thresholds and is non iterative. The red and blue channels are then refined using structural approximation. Index Terms Multiscale color gradients, Color Filter Array (CFA) interpolation, demosaicing, directional interpolation. 1. INTRODUCTION emosaicing algorithm is a digital image process used to reconstruct a full color image from the incomplete color samples obtained from an image sensor overlaid with a color filter array (CFA). Also known as CFA interpolation or color reconstruction [21] .The reconstructed image is typically accurate in uniform-colored areas, but has a loss of resolution and has edge artifacts in non uniform-colored areas. A color filter array is a mosaic of color filters in front of the image sensor. The most commonly used CFA configuration is the Bayer filter shown in Fig 1.1. This has alternating red (R) and green (G) filters for odd rows and alternating green (G) and blue (B) filters for even rows. There are twice as many green filters as red or blue ones, exploiting the human eye's higher sensitivity to green light. Figure 1.1: Bayer mosaic of color image 1.1 Existing Algorithms Nearest neighbor interpolation simply copies an adjacent pixel of the same color channel (2x2 neighborhood). It is unsuitable for any application where quality matters, but can be used for generating previews with given limited computational resources [25].In bilinear interpolation, the red value of a non-red pixel is computed as the average of the two or four adjacent red pixels. The blue and green values are also computed in a similar way. Bilinear interpolation generates significant artifacts, especially across edges and other high-frequency content, as it doesn`t take into account the correlation between the RGB values [22]. Cubic interpolation takes into account more neighbors than in algorithm no. [22] (e.g., 7x7 neighborhood). Lower weight is given to pixels which are far from the current pixel.Gradient- corrected bilinear interpolation assumes that in a luminance/chrominance decomposition, the chrominance components don`t vary much across pixels. It exploits the inter- channel correlations between the different color channels and uses the gradients among one color channel, to correct the bilinearly interpolated value [23]. Smooth hue transition interpolation assumes that hue is smoothly changing across an objects surface; simple equations for the missing colours can be obtained by using the ratios between the known colours and the interpolated green values at each pixel [22]. Problem can occur when the green value is 0, so some simple normalization methods are proposed [24].In order to prevent flaws when estimating colours on or around edges, pattern recognition interpolation [3] describes a way to classify and interpolate three different patterns (edge, corner and strip) in the green color plane that are shown in Fig 1.2. The first step in this procedure is to find the average of the four neighboring green pixels, and classify the neighbors as either high or low in comparison to this average. . D
  2. 2. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 1 APRIL 2015 - ISSN: 2349 - 9303 91 Figure 1.2: (a) is a high edge pattern, (b) is a low edge pattern, (c) is a corner pattern, and (d) is a stripe pattern. Adaptive color plane interpolation assumes that the color planes are perfectly correlated in small enough neighborhoods [25]. That is, in a small enough neighborhood, the equations. G = B + k G = R + j are true for constants k, j. In order to expand the edge detection power of the adaptive color plane method, it is prudent to consider more than two directions (i.e., not only the horizontal and vertical directions). Thus directionally weighted gradient based interpolation uses information from 4 directions (N, S, W, and E as shown in Figure1.3) Figure 1.3: Neighborhood of B pixel A weight is assigned for each direction, using the known information about the differences between B and G value [25]. 2. PROPOSED SYSTEM DESIGN 2.1. System Description The first step of the algorithm is to get initial directional color channel estimates. The quality can be improved by applying the interpolation over color differences using the advantages of correlation between the color channels. Now every pixel location has a true color channel value and two directional estimates. By taking their difference, the directional color difference estimated. The next step of the algorithm is to reconstruct the green image along horizontal and vertical directions. Once the missing green component is interpolated, the same process is performed for estimating the next missing green component in a raster scan manner. After interpolating all missing green components of the image, the missing red and blue components at green CFA sampling positions are estimated. Next, the directional color difference estimates are combined from different directions. The directional CFA interpolation method is based on multi scale color gradients. Gradients are useful for extracting directional data from digital images. In this method, the horizontal and vertical color difference estimates are blended based on the ratio of the total absolute values of vertical and horizontal color difference gradients over a local window. For red & green rows and columns in the input mosaic image, the directional estimates for the missing red and green pixel values are estimated by initial directional color channel estimates. The color difference gradients calculated are used to find weights for each direction. In order to avoid repetitive weight calculations, the directional weights are reused. Then the artifacts are removed and red and blue channels are refined by the Structural Approximation method. The modules of the proposed system framework are illustrated in Fig 2.1. Fig 2.1 System Framework 2.1.1. Initial Directional Color Channel Estimation To obtain a full color image, various demosaicing algorithms can be used to interpolate a set of complete red, green, and blue values for each point. The directional estimates for the missing red and green pixel values, for red and green rows and columns in the input mosaic image, are calculated. The directional estimates for the missing blue and green pixel values, for blue and green rows and columns in the input mosaic image are calculated. Then horizontal and vertical color channel estimates are calculated for finding directional color channel estimates. The directional color channel estimates for the missing green pixel values are, , = , 1 + , + 1 2 + 2. , , 2 , + 2 4 (1) , = 1, + ( + 1, ) 2 + 2. , 2, ( + 2, ) 4 (2)
  3. 3. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 1 APRIL 2015 - ISSN: 2349 - 9303 92 Here, , - Horizontal green color channel estimation at red pixel , - Vertical green color channel estimation at red pixel The color channel estimates are calculated from the Bayer pattern. Here H and V denotes horizontal and vertical directions and (i,j) denotes the pixel location. 2.1.2. Directional Color Difference Estimation The quality can be improved by applying the interpolation over color differences to take advantage of the correlation between the color channels. This is an important technique employed in the reconstruction of full color images, obtained by interpolation along horizontal and vertical direction. Every pixel coordinate has a true color channel value and two directional estimates. By taking their difference directional color difference estimated. Cg,r H i,j = gH i,j -R i,j , if G is interpolated G i,j -rH i,j , if R is interpolated (3) Cg,r V i,j = gV i,j -R i,j , if G is interpolated G i,j -rV i,j , if R is interpolated (4) , , , , , are the horizontal and vertical difference estimates between green and red channels. 2.1.3. Multiscale Gradient Calculation A full-color image is usually composed of three color planes. Three separate sensors are required for a camera to measure an image. To reduce the cost, many cameras use a single sensor overlaid with a color filter array. The most commonly used CFA nowadays is the Bayer CFA. In a single sensor digital camera, only one color is measured at each pixel and the other two missing color values are estimated. This estimation process is known as color demosaicing. The Bayer pattern is comprised of blue and green and red and green rows and columns as shown in Fig 2.2. To obtain a full- color image, various demosaicing algorithms can be used to interpolate a set of complete red, green, and blue values for each point.For red and green rows and columns in the input mosaic image, the directional estimates for the missing red and green pixel values are calculated . Fig 2.2 Bayer pattern The quality can be improved by applying the interpolation over color differences to take advantage of th