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Multiscale Gradient Based – Directional CFA Interpolation with Refinement

Nov 11, 2015

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Abstract—Single 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.

  • INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 1 APRIL 2015 - ISSN: 2349 - 9303

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    Multiscale Gradient Based Directional CFA Interpolation with Refinement

    Aarthy Poornila.A1

    1Mepco Schlenk Engineering

    College,

    ECE Department

    [email protected]

    R. Mercy Kingsta2

    Assistant Professor 3Mepco 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

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    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)

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    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,rH i,j =

    gH i,j -R i,j , if G is interpolated

    G i,j -rH i,j , if R is interpolated (3)

    Cg,rV i,j =

    gV i,j -R i,j ,