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

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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

  • 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

  • Example

De-saturated

Saturated


Motivation

Motivation

  • Motivation

    • Causes loss of data

    • Visually unpleasant

    • Recapturing not always a solution


Color channel saturation

Color Channel Saturation

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

Green

Red

Blue


Smoothness constraint

Images are locally smooth except across edges

Smoothness Constraint


Cross channel ratios

Cross-Channel Ratios

  • Cross-channel ratios are locally smooth

1-pixel vertical shift

3-pixel horizontal shift

5-pixel diagonal shift


Fundamentals

Fundamentals

  • 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

  • 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

  • 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

  • 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

  • Example

    • Red:Green ratio

Saturated ratio

Ground Truth ratio

Recovered ratio


Step 2 estimating color values

Step 2: Estimating Color Values

  • 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’

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’

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

  • 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

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


Quantitative analysis

Quantitative Analysis

  • 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

Saturated image

Ground truth image

Recovered image


Quantitative analysis2

Quantitative Analysis

Comparison of saturated region (forehead)

Quantitative comparison


Comparison with baseline methods

Comparison with Baseline Methods

  • 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

Pixel-wise non-linearity

Our method

Saturated image


Comparison levin s colorization

Comparison: Levin’s Colorization

Colorization

Our method

Saturated image


Non face images

Non-face Images


Results

Results

Saturated

De-saturated


Results1

Results

Saturated

De-saturated


Results2

Results

Saturated

De-saturated


Results3

Results

Saturated

De-saturated


Results4

Results

Saturated

De-saturated


Automatic correction of saturated regions in photographs using cross channel correlation

Q & A


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