EE 7700 Demosaicking Problem in Digital Cameras. Bahadir K. Gunturk2 Multi-Chip Digital Camera Lens...

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Transcript of EE 7700 Demosaicking Problem in Digital Cameras. Bahadir K. Gunturk2 Multi-Chip Digital Camera Lens...

EE 7700

Demosaicking Problem in Digital Cameras

Bahadir K. Gunturk 2

Multi-Chip Digital Camera

Lens

Scene

Spectral

filters

Beam-splitter

s

Sensors

To produce a color image, at least three spectral components are needed at each pixel.

One approach is to use beam-splitters and multiple chips.

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Single-Chip Digital Camera Multi-chip approach is expensive. Precise chip alignment is

required. The alternative is to use a color filter array.

Lens

Scene

Color filter array

Sensors

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Single-Chip Digital Camera The missing color samples must be estimated to produce

the full color image. Since a mosaic of samples are available, this estimation

(interpolation) process is called demosaicking.

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Single-Chip Digital Camera Images suffer from color artifacts when the samples are not

estimated correctly.

Original image Bilinearly interpolated from CFA-filtered

samples

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Demosaicking Approaches Non-Adaptive Single-Channel Interpolation: Interpolate

each color channel separately using a standard technique, such as nearest-neighbor interpolation, bilinear interpolation, etc.

Edge-Directed Interpolation: Estimate potential edges, avoid interpolating across the edges.

1 2

3

4

x

Edge-directed interpolation 1.Calculate horizontal gradient ΔH = |G1 – G2| 2.Calculate vertical gradient ΔV = |G3 – G4|3.If ΔH > ΔV,Gx = (G3 + G4)/2 Else if ΔH < ΔV, Gx = (G1 + G2)/2 Else Gx = (G1 + G2 + G3 + G4)/4

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Demosaicking Approaches Edge-Directed Interpolation: Based on the assumption that

color channels have similar texture, various edge detectors can be used.

Edge-directed interpolation 1. Calculate horizontal gradient ΔH = | (R3 + R7)/2 – R5 | 2. Calculate vertical gradient ΔV = | (R1 + R9)/2 – R5 | 3. If ΔH > ΔV,

G5 = (G2 + G8)/2 Else if ΔH < ΔV, G5 = (G4 + G6)/2 Else G5 = (G2 + G8 + G4 + G6)/4

1

2

4 5 6 7

8

9

3

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Demosaicking Approaches Constant-Hue-Based Interpolation: Hue does not change

abruptly within a small neighborhood. Interpolate green channel first. Interpolate hue (defined as either color differences or color

ratios). Estimate the missing (red/blue) from the interpolated hue.

Red Interpolated Red

InterpolateGreen

Interpolate

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Demosaicking Approaches Edge-Directed Interpolation of Hue: It is a combination of

edge-directed interpolation and constant-hue-based interpolation. Hue is interpolated as in constant-hue-based interpolation approach, but this time, hue is interpolated based on the edge directions (as in the edge-directed interpolation algorithm).

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Demosaicking Approaches Using Laplacian For Enhancement: Use the second-order

gradients of red/blue channels to enhance green channel.

1

2

4 5 6 7

8

9

3

1. Calculate horizontal gradient ΔH = |G4 – G6| + |R5 – R3 + R5 – R7| 2. Calculate vertical gradient ΔV = |G2 – G8| + |R5 – R1 + R5 – R9| 3. If ΔH > ΔV,

G5 = (G2 + G8)/2 + (R5 – R1 + R5 – R9)/4Else if ΔH < ΔV,

G5 = (G4 + G6)/2 + (R5 – R3 + R5 – R7)/4 Else

G5 = (G2 + G8 + G4 + G6)/4 + (R5 – R1 + R5 – R9 + R5 – R3 + R5 – R7)/8

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Aliasing

mf 1f

2f

2f

1f

2f

1f

Green channel Red/Blue channel

Frequency spectrum of an image:

After CFA sampling:

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Demosaicking Approach Alias Cancelling: Based on the assumption that red, green,

and blue channels have similar frequency components, the high-frequency components of red and blue channels are replaced by the high-frequency components of green channel.

2f

1f

Red/Blue channel

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Experiment

Full Red/Green/Blue

channels

Subband decomposition

CFA Sampling

Subband decomposition

Interpolate

LL

HL

HH

LH

LLLL

HLHL

LHLH

LL

HL

HH

LH

LLLL

HLHL

LHLH

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Constraint Sets

Detail Constraint Set: Detail subbands of the red and blue channels must be similar to the detail subbands of the green channel.

HL

HH

LH

HL

LH

HH

HLRHLG

1 2 1 2 1 2 1 2( , ) : ( , ) ( , ) ( , ),

, ,k k

d

R n n R n n G n n T n nC

for k HL LH HH

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Constraint Sets

Observation Constraint Set: Interpolated channels must be consistent with the observed data.

CFASensors

1 2( , )O n n

R

1 2 1 2 1 2 1 2( , ) : ( , ) ( , ), ( , )o RC R n n R n n O n n n n

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HL

LH

HH

Projection Operations

Projection onto the Detail Constraint Set:

Decompose the color channels.

Update the detail subbands of red and blue channels.

1 2( , )HLG n n

1 2 1 2( , ) ( , )HLG n n T n n

Apply synthesis filters to reconstruct back the channels.

1 2( , )HLR n n

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Projection Operations

Projection onto the Observation Constraint Set:

Insert the observed data to their corresponding positions.

CFASensors

1 2( , )O n n

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Alternating Projections Algorithm

Samples of color channels

Initial interpolation

0h

1h

0g

1gUpdate

Insert the observed data

Projection onto the detail constraint set

Projection onto the observation constraint set

Iteration

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Results

Original Hibbard 1995 Laroche and Prescott 1994

Hamilton and Adams 1997 Kimmel 1999 Gunturk 2002

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Results

OriginalHibbard

1995

Laroche and

Prescott 1994

Hamilton and

Adams 1997

Kimmel 1999

Gunturk 2002

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Previous Methods

Gunturk et al, “Demosaicking: Color Filter Array Interpolation in Single-Chip Digital Cameras,” to appear in IEEE Signal Processing Magazine.

[Gunturk02]

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References

[Gunturk02] Gunturk et al, “Color Plane Interpolation Using Alternating Projections,” IEEE Trans. Image Processing, 2002.

[Hibbard 1995] R. H. Hibbard, “Apparatus and method for adaptively interpolating a full color image utilizing luminance gradients,” U.S. Patent 5,382,976, January, 1995.

[Laroche and Prescott 1994] C. A. Laroche and M. A. Prescott, “Apparatus and method for adaptively interpolating a full color image utilizing chrominance gradients,” U.S. Patent 5,373,322, December, 1994.

[Hamilton and Adams 1997] J. F. Hamilton Jr. and J. E. Adams, “Adaptive color plane interpolation in single sensor color electronic camera,” U.S. Patent 5,629,734, May, 1997.

[Kimmel 1999] R. Kimmel, “Demosaicing: Image reconstruction from CCD samples,” IEEE Trans. Image Processing, vol. 8, pp. 1221-1228, 1999.