Final Synopsis Project Demosaicing

7/30/2019 Final Synopsis Project Demosaicing

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Synopsis

of

Minor Project Work

on

Artifacts of Image Demosaicing using Bilinear interpolation

Master of TechnologyIn

Electronics and Communication Engineering

Under the guidance of

Mr. Jawed Ashraf

Submitted by

Ishita Aggarwal

Roll No. MTE1/10/05

Alfalah school of engineering and technology

Dhauj,faridabad


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TITLE OF PROJECT

Artifacts Of Image Demosaicing Using Bilinear Interpolation.

INTRODUCTION

Cost effective digital cameras use a single-image sensor, applying

alternating patterns of red, green, and blue color filters to each pixel

location. The problem of reconstructing a full three-color representation of

color images by estimating the missing pixel components in each color plane

is called demosaicing. In this project we will examine a traditional method

of demosaicking i.e. Bilinear Interpolation and will find out the resulting

artifacts.

OBJECTIVE

1. Study of image Demosaicing process.

2. Study of Various algoritms of Image Demosaicing.

3. Implementation of algoritms in matlab

4. Implementation of suitable algorithm on different images

5. Performance Analysis

SOFTWARE REQUIREMENT

Matlab Software


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Image Demosaicing

The problem of reconstructing a full three-color representation of color

images by estimating the missing pixel components in each color plane is

called demosaicing.

When an image is captured by a monochrome camera, a single charge-

coupled device (CCD) or Complementary metal-oxide semiconductor

(CMOS) sensor is used to sample the light intensity projected onto the

sensor. Color images are captured in much the same way, except that the

light Intensity is measured in separate color bands, usually red, green, and

blue.

In order to do this, three separate sensors could be used in conjunction with a

beam splitter to accurately measure each of the three primary colors at each

pixel. However, this approach is expensive and mechanically difficult to

implement, making its use in commercial imaging systems infeasible. To

overcome this obstacle, the color filter array (CFA) was introduced to

capture a color image using only one sensor.


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A CFA is an array of alternating color filters that samples only one color

band at each pixel Location. The most popular CFA pattern is the Bayer

pattern (figure 1), which features blue and Red filters at alternating pixel

locations in the horizontal and vertical directions, and green filtersorganized in the quincunx pattern at the remaining locations (1). This

pattern results in half of the image resolution being dedicated to accurate

measurement of the green color band. The peak Sensitivity of the human

visual system lies in the medium wavelengths, justifying the extra green

Sampling (2). Because each pixel now has only one color sampled, a

demosaicing algorithm must be employed to recover the missing

information.

Demosaicing Algorithms

The use of Bayer matrices simplifies the semiconductor and hardware parts

of image capture, but adds to the complexity of image processing. The

problem, as can be seen in Figure 2 below is how to best reconstruct the

original image from the Bayer sampled image. This section reviews of some

of the traditional algorithms used for Bayer Demosaicing.

Fig 2


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Non-Adaptive Algorithms

Non-adaptive demosaicing algorithms are algorithms that do not take into

account the specific photometric pattern content of the mosaic that is beingprocessed. They interpolate the picture's missing color elements the picture

by averaging the neighbor pixels in adjacent regions, generally of the same

color. They are simple to implement and have low computational time

requirements. Three such algorithms we look at here are nearest neighbor,

bilinear interpolation.

Nearest Neighbor hood

The simplest algorithm for demosaicing is nearest neighbor. Nearestneighbor assigns a color value with the nearest known red, green or blue

pixel value in the same color plane. There is usually some ordering as to

which nearest neighbor to use (left, right, top, or below) for the particular

implementation. For example, for a 3x3 block in green plane, we assume the

left neighboring pixel value is used to fill the missing ones.

Bilinear Interpolation

Bilinear interpolation goes one step further from Nearest Neighbor

Interpolation by taking the average value of all the nearest neighbors. For

instance, the Bayer matrix in Figure


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will have a green value for B7 (which is mosaicked as a blue pixel) of the

average of adjacent green values namely G2, G6, G8, and G12. For

interpolation of red/blue pixels at a green position, the average of the two

adjacent pixels of the same color is assigned to the interpolated pixel. Thus,

for example: B8 = B7 + B9, and R8=R3+R13.One benefit of this method is

that it can be performed by a convolution with the appropriate kernel. Two

kernels are required: one for estimating the missing green values and one for

estimating missing red/blue values.

This interpolation method performs well in smooth areas where the color

changes slowly from one to the next. However, when performed along edges

where color changes occur abruptly, false color and zipper artifacts are

introduced, resulting in a poor quality image.

Smooth Hue Transition

Smooth Hue Transition is an elaboration on bilinear interpolation in that this

algorithm takes into account hue transition. One problem with the bilinear

interpolation is that the hues of adjacent pixels change abruptly and

unnaturally. Smooth hue transition attempts to deal with this problem by

treating the green channel as a luminosity channel and the red and blue

channels as chromaticities. Consequently, the algorithm demosaicks the

three colors differently. For the green, luminous channel, a bilinear

interpolation scheme is undertaken.


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Adaptive Algorithms :

Adaptive algorithms try to take into account spatial information of a specific

image. Most of these algorithms use threshold values such that the algorithm

can make intelligent decision as to which neighbor pixel values to average.

Edge Sensing Interpolation

The edge sensing adaptive algorithm uses a set of threshold values to

determine whether to average adjacent pixels on the right and left side or

adjacent pixels on the top and bottom side of the pixel being interpolated. As

the name alludes to, this algorithm is especially important in demosaicking

edges within a picture.

Essentially the algorithm determines where a particular direction of adjacent

pixels (top-bottom or left-right) is exclusively greater than a given threshold

value, as shown in Equation 1 below. If this is the case, then most likely a

line or edge exists and therefore when averaging adjacent pixels for

demosaicking, it is best to not smooth in the direction where the gradient

values are higher than a given threshold value. Where this method fails is

along diagonal lines, since the gradients are only taken along the horizontal

and vertical directions.


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Common Demosaicing Artifacts

Because sampling a scene using a CCD with a Bayer pattern CFA measures

only 33% of the information of the original scene, several artifacts occur as a

result of demosaicing. Some of the most common are:

False Color Effect

Zippering Effect

Excessive Blurring

Aliasing

False Color Artifact

A frequent and unfortunate artifact of CFA demosaicing is what is known as

false coloring. This Artifact typically manifests itself along edges, where

abrupt or unnatural shifts in color occur as a result of mis-interpolating

across, rather than along, an edge.

Zippering Artifact

Another side effect of CFA demosaicing, which also occurs primarily along

edges, is known as the zipper effect. Simply put, zippering is another namefor edge blurring that occurs in an on/off pattern along an edge. This effect

occurs when the demosiacing algorithm averages pixel values over an edge,

especially in the red and blue planes, resulting in its characteristic blur.


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Excessive Blurring

Another artifact of demosaicing is Blurring effect.


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REFERENCES

Bayer B.E., Color imaging array, U.S. Patent 3 971 065, July 1976.

D. Menon, S. Andriani and G. Calvagno, A Novel Technique For

Reducing Demosaicing Artifacts, Proc. of the 14th European Signal

Processing Conf.(Eusipco), Sept. 2006

Gonzalez, Rafael C., Woods, Richard E., Eddins, Steven L., Digital

Image Processing using Matlab: Pearson Prentice Hall, 2004.

R. Kimmel, Demosaicing: Image reconstruction from ccd

samplesIEEE Trans. Image Processing, vol. 8, no. 9, pp. 1221-

1228, Sept. 1999.

H. J. Trussell and R. E. Hartwig, Mathematics for

demosaicking IEEE Trans. Image Processing, vol. 3, no. 11, pp.

485-492, Apr. 2002.
http://www.danielemenon.it/pub/rda/rda.phphttp://www.danielemenon.it/pub/rda/rda.phphttp://www.danielemenon.it/pub/rda/rda.phphttp://www.danielemenon.it/pub/rda/rda.php

Final Synopsis Project Demosaicing

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