Color Restoration Techniques for Faded Colors of Old Photos, Printings and Paintings

Transcription

1 Color Restoration Techniques for Faded Colors of Old Photos, Printings and Paintings Electro/Information Technology, IEEE International Conference Ayman M. T. Ahmed Presented by Dae Chul Kim School of Electrical Engineering and Computer Science Kyungpook National Univ.

2 Abstract Proposed Color restoration for old picture, printings, slides film and paintings Two approaches Combination of the standard deviation-weighted gray world and the Combined Gray World and Retinex(CGWR) techniques Modification of the Multi Scale Retinex(MSR)» These modification are developed to imitate the effect of the neighboring pixels on the human eye 2 /20

3 Introduction Faded color of old picture, printings, slides film and paintings Reasons Usually unknown Chemical and physical changes Captured film and pictures in poor recording conditions Problem of amount of damage Color fading is proportional to time period Fading percentage is not equally distribute over the all spectral channels 3 /20

4 The techniques to enhance and restore lost colors Gray world Using average value Retinex Using maximum value Combined Gray World and Retinex(CGWR) Compensation for the limitation of two techniques Max white Standard Deviation-Weighted Gray World(SDWGW) 4 /20

5 Proposed method Two color restoration techniques Combination of the SDWGW and the CGWR Subdivided into small blocks The gains of the three RGB channels are calculated as a function of the mean and standard deviation of each block The output of SDWGW is introduced to combined gray world and retinex algorithm Modification of the original CGWR using Multi Scale Retinex(MSR) Input image is processed by modified MSR Output process is introduced to traditional CGWR color restoration results 5 /20

6 Combined SDWGW_CGWR Standard deviation-weighted gray world Enhancement of gray world color restoration algorithm In this technique Subdividing original input image into n blocks Calculation of standard deviation and means for three RGB channels on each block Standard deviation weighted average for red channel SDWA n Re d l = 1 Gain for the red channel σ R () l = µ R() l n σ ( j) (1) j= 1 R Re d gain SDWA + SDWA + SDWA Re d Green Blue = (2) SDWA Re d 6 /20

7 Combined gray world and retinex limitations of retinex and gray world color restoration skill Failure in handling all image portions with zero intensity Failed to map the zero pixels into restored color values CGWR algorithm Using quadratic equation to perform the mapping and color correction Assumption Green channel never changes 7 /20

8 Restored values of red channel ' 2 R xy R xy λrxy (, ) = I (, ) + (, ) (3) To meet the average assumptions of the gray world technique M N 2 R( xy, ) λ M N Rxy (, ) M N R'( xy, ) (4) x= 1 y= 1 x= 1 y= 1 x= 1 y= 1 I + = To satisfy the maximum assumptions of retinex technique I max ( R( xy, )) + λ max ( Rxy (, )) = max ( R'( xy, )) (5) 2 xy, xy, xy, Eq.(4) and (5) are solved together for the two variables M N 1 2 M N M N R( xy, ) Rxy (, ) R'( xy, ) x= 1 y= 1 x= 1 y= 1 x= 1 y= 1 = 2 max xy, ( R( xy, )) max xy, ( Rxy (, )) max xy, ( R'( xy, )) (6) I λ 8 /20

9 SDWGW_CGWR color restoration technique First stage Image enhancement To increase the dynamic range representation of the included color components Image subdivision into n blocks Number of block is image dependant based on colors distribution and size of each color locality in input image 9 /20

10 Second stage Output of the subdivision is introduced to the SDWGW algorithm Calculation of variances and means of each channel for each block Calculation of weighted-averages for three channels Gains for the three channels are calculated to compensate for the missing color values Third stage Simply the CGWR algorithm Using quadratic equations to perform the mapping and color corrections To satisfy them maximum and average requirements of retinex and gray world 10 /20

11 Fig. 1. Flowchart of the proposed SDWGW_ CGWR color restoration approach. 11 /35

12 Result from proposed SDWGW_CGWR 12 /35 Fig. 2. Results from the proposed SDWGW_CGWR color restoration technique: (a) Input faded colors image, (b) Output of the SDWGW only, (c) Output of the CGWR only, (d) Output of the combined SDWGW_CGWR.

13 Combined Gray World and Multi Scale Retinex Multi scale retinex Single scale retinex(ssr) ret( xy, ) = log[ I( xy, )] log[ Fxy (, ) I( xy, )] (7) i i i Where reti ( x, y) is the retinex output, I is input image, i ( xy, ) i ( RGB,, ) is convolution operation spectral channels and Gaussian surround function F( x, y) Ke + ( x2 y2)/ c2 = (8) Where c is the Gaussian constant, which specifies the scale of the F function K is selected to normalize the total energy in surrounding Gaussian function to one 13 /20

14 Multi scale retinex Weighted sum of several SSR output ret N = w ret (9) MSRi n= 1 n ni Where N is the number of selected scales is the weight associated with each scale, which is usually fixed to 1/N w n 14 /20

15 Combined gray world and multi scale retinex color restoration technique Based on the original combined gray world and retinex First stage The input damaged image is first preprocessed by surrounding Gaussian function to imitate the retinex response to the neighboring pixels Second stage I_ SSRxy (, ) = Fxy (, ) I( xy, ) (10) i I 1 _ MSRxy N i(, ) = Fxy (, ) I ( xy, ) n= 1 i N Output of the MSR is introduced to the traditional CGWR color restoration process i (11) 15 /20

16 Fig. 3. Flowchart of the proposed CGW_MSR color restoration approach. 16 /20

17 Result from the proposed CGW_MSR Fig. 4. Results from the proposed CGW_MSR color restoration technique: (a) Input faded colors image, (b) Output of the CGW_SSR, (c) Output of the CGW_MSR color restoration technique. 17 /20

18 Evaluation Evaluation Evaluation is performed by human observers Limitation of human eye in detecting all changes in color Highly textured image regions Use of simple color distance metrics based on space CIELab color The image is first transformed to the CIELab color space ab plane includes all the chromaticity information of the image Generally, faded image usually include high cast represented by some big distance values Dist = µ / σ (12) Where µ = µ + µ 2 2 a b, and σ = σ + σ 2 2 a b 18 /20

19 Table 1. Numerical evaluation for the first proposed color restoration approach (SDWGW_CGWR) based on the cast calculations for a set of 5 different test image Table 2. Numerical evaluation for the second proposed color restoration approach (CGW_MSR) based on the cast calculations for a set of 5 different test image 19 /20

20 Conclusions Proposed algorithm Two color restoration techniques SDWGW_CGWR Combination of standard deviation-weighted average gray world, and retinex theory CGW_MSR Modification of the standard CGWR Use of MSR to imitate the effect of the neighboring pixels on the human eye Better color restoration results beyond the limited capabilities of basi 20 / 20