Automatic correction of saturated regions in photographs using cross channel correlation
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Automatic Correction of Saturated Regions in Photographs using Cross-Channel Correlation. Syed Zain Masood, Jiejie Zhu & Marshall F. Tappen. University of Central Florida (UCF) Orlando, FL, USA. Introduction. What is Saturation ? Caused by over-exposure

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Automatic correction of saturated regions in photographs using cross channel correlation

Automatic Correction of Saturated Regions in Photographs using Cross-Channel Correlation

Syed Zain Masood, Jiejie Zhu & Marshall F. Tappen

University of Central Florida (UCF)

Orlando, FL, USA


Introduction
Introduction using Cross-Channel Correlation

  • What is Saturation ?

    • Caused by over-exposure

    • Sensor incorrectly responds at its maximum value

    • Incorrect camera exposure settings main cause of saturation

    • Undesirable artifacts


Saturation free image
Saturation-free Image using Cross-Channel Correlation

  • Example

De-saturated

Saturated


Motivation
Motivation using Cross-Channel Correlation

  • Motivation

    • Causes loss of data

    • Visually unpleasant

    • Recapturing not always a solution


Color channel saturation
Color Channel Saturation using Cross-Channel Correlation

  • For most cases, only 1 or 2 channels are saturated

Green

Red

Blue


Smoothness constraint

Images are locally smooth except across edges using Cross-Channel Correlation

Smoothness Constraint


Cross channel ratios
Cross-Channel Ratios using Cross-Channel Correlation

  • Cross-channel ratios are locally smooth

1-pixel vertical shift

3-pixel horizontal shift

5-pixel diagonal shift


Fundamentals
Fundamentals using Cross-Channel Correlation

  • Estimate cross-channel ratios using smoothness prior.

  • Use cross-channel correlation plus smoothness constraint in recovering true color values for saturated pixels.


Recovery method
Recovery Method using Cross-Channel Correlation

  • Two step method

    • Step 1: Recover cross-channel ratios

      • Estimate the color channel ratios for saturated pixels

    • Step 2: Recover color channel values

      • Using the true cross-channel ratios calculated in step 1, estimate the correct responses for saturated color channels


Step 1 estimating cross channel ratios
Step 1: Estimating Cross-Channel Ratios using Cross-Channel Correlation

  • Recovering cross-channel ratios

    • Saturated color channel ratio estimated using local smoothness constraint.

    • Unsaturated color channel ratio close to observed

    • Quadratic cost function used

    • Solution obtained using classical energy minimization method


Step 1 constraints
Step 1: Constraints using Cross-Channel Correlation

  • Smoothness constraint for estimating ratios involving saturated channels

  • Ratio between unsaturated channels close to observed

Weight function (Similar to Levin’s Colorization)

  • Constraints

Color Channel Ratio e.g. Red/Green

Original Color Channel Ratio e.g. Red/Green


Step 1 cost function

Weight function (Similar to Levin’s Colorization)

0 if pixel ‘p’ saturated, 1 otherwise

Estimated Color Channel ratio at pixel ‘p’

Observed Color Channel ratio at pixel ‘p’

Estimated Color Channel ratio at pixel ‘q’

Step 1: Cost Function

  • Cost function:


Step 1 example
Step 1: Example Colorization)

  • Example

    • Red:Green ratio

Saturated ratio

Ground Truth ratio

Recovered ratio


Step 2 estimating color values
Step 2: Estimating Color Values Colorization)

  • Estimating Color Values

    • Partially Saturated Pixels: Cross-channel ratios used to estimate color values

    • Totally Saturated Pixels: Estimated using the local smoothness constraint

    • Unsaturated color values close to observed

    • Quadratic cost function used

    • Solution obtained using classical energy minimization

    • For simplicity, recovery of Red channel shown.


Step 2 constraints

Observed Red value at pixel ‘p’ Colorization)

Step 2: Constraints

  • Constraints

    • Partially Saturated: Values estimated using cross-channel ratios

    • Totally Saturated: Est. using local smoothness

    • Unsaturated values close to observed

Red:Green at pixel ‘p’

Red:Blue at pixel ‘p’


Step 2 cost function

Color ratio Red:Green at pixel ‘p’ Colorization)

Color ratio Red:Blue at pixel ‘p’

Observed Red value at pixel ‘p’

‘1’ if at pixel ‘p’: Red is saturated Green is unsaturated

‘1’ if at pixel ‘p’: Red is saturated Blue is unsaturated

‘1’ if at pixel ‘p’: Red is unsaturated

‘1’ if at pixel ‘p’: All channels saturated

Step 2: Cost Function

  • Cost function:

Red value at pixel ‘q’ in N(p)


Experiments results
Experiments & Results Colorization)

  • System evaluated both quantitatively and qualitatively

  • Comparison against pixel-wise scaling and Levin’s colorization demonstrated.

  • 3x3 neighborhood window used

  • Image size typically 500x400 or less

  • Matlab built-in least square solver used


Limitations
Limitations Colorization)

  • Handling totally saturated pixels in a significant portion of the image


Quantitative analysis
Quantitative Analysis Colorization)

  • Auto-exposure bracketing feature in modern SLR cameras used

  • Obtained two identical images with different exposure settings

  • The brighter image is saturated while the darker is the ground truth


Quantitative analysis1
Quantitative Analysis Colorization)

Saturated image

Ground truth image

Recovered image


Quantitative analysis2
Quantitative Analysis Colorization)

Comparison of saturated region (forehead)

Quantitative comparison


Comparison with baseline methods
Comparison with Baseline Methods Colorization)

  • Baseline Methods

    • Pixel-wise non-linearity

    • Levin’s Colorization

    • We would show that comparable saturation-free results cannot be achieved by the above techniques.


Comparison pixel wise non linearity
Comparison: Pixel-wise Non-linearity Colorization)

Pixel-wise non-linearity

Our method

Saturated image


Comparison levin s colorization
Comparison: Levin’s Colorization Colorization)

Colorization

Our method

Saturated image


Non face images
Non-face Images Colorization)


Results
Results Colorization)

Saturated

De-saturated


Results1
Results Colorization)

Saturated

De-saturated


Results2
Results Colorization)

Saturated

De-saturated


Results3
Results Colorization)

Saturated

De-saturated


Results4
Results Colorization)

Saturated

De-saturated


Q & A Colorization)


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