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EE 7700 Demosaicking Problem in Digital Cameras
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Bahadir K. Gunturk2 Multi-Chip Digital Camera Lens Scene Spectral filters Beam- splitters 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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Bahadir K. Gunturk3 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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Bahadir K. Gunturk4 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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Bahadir K. Gunturk5 Single-Chip Digital Camera Images suffer from color artifacts when the samples are not estimated correctly. Original imageBilinearly interpolated from CFA-filtered samples
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Bahadir K. Gunturk6 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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Bahadir K. Gunturk7 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 56 7 8 9 3
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Bahadir K. Gunturk8 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 Interpolate d Red Interpolate Green Interpolate
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Bahadir K. Gunturk9 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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Bahadir K. Gunturk10 Demosaicking Approaches Using Laplacian For Enhancement: Use the second-order gradients of red/blue channels to enhance green channel. 1 2 4 56 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)/4 Else 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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Bahadir K. Gunturk11 Aliasing Green channel Red/Blue channel Frequency spectrum of an image: After CFA sampling:
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Bahadir K. Gunturk12 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. Red/Blue channel
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Bahadir K. Gunturk13 Experiment Full Red/Green/Blue channels Subband decomposition CFA Sampling Subband decomposition Interpolate LL HL HH LH LL HL LH LL HL HH LH LL HL LH
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Bahadir K. Gunturk14 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
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Bahadir K. Gunturk15 Constraint Sets Observation Constraint Set: Interpolated channels must be consistent with the observed data. CFA Sensors
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Bahadir K. Gunturk16 HL LH HH Projection Operations Projection onto the Detail Constraint Set: Decompose the color channels. Update the detail subbands of red and blue channels. Apply synthesis filters to reconstruct back the channels.
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Bahadir K. Gunturk17 Projection Operations Projection onto the Observation Constraint Set: Insert the observed data to their corresponding positions. CFA Sensors
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Bahadir K. Gunturk18 Alternating Projections Algorithm Samples of color channels Initial interpolation Update Insert the observed data Projection onto the detail constraint set Projection onto the observation constraint set Iteration
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Bahadir K. Gunturk19 Results OriginalHibbard 1995Laroche and Prescott 1994 Hamilton and Adams 1997 Kimmel 1999Gunturk 2002
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Bahadir K. Gunturk20 Results Original Hibbard 1995 Laroche and Prescott 1994 Hamilton and Adams 1997 Kimmel 1999 Gunturk 2002
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Bahadir K. Gunturk21 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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Bahadir K. Gunturk22 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.
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