enhancement of backlight scaled images

ENHANCEMENT OF BACKLIGHT SCALED IMAGES

P. VIBHA BHANDARY4SF10EC101

INTRODUCTION

Goal- to reduce undesired effects of dim backlight on image quality with backlight 10% or less.

Human Visual System( HVS)- a perception based approach.

Luminance < threshold => image detail invisibility

INTRODUCTION

BACKGROUND

1) Just Noticeable Difference (JND):

1. Linear curve-behavior

2. Smallest difference in the sensory input discernible by human being.

3. ΔL= J(L)=0.0594(1.219+L 0.4 )2.5

L= Background luminance

2) Human Visual Response Model:

1. Nonlinear curve-behavior

2. Li= Li-1 +J( Li-1) , L>0

3. Li reaches upper bound of luminance range

3) Effects of dim backlight on images:

V/Vm = normalized response

I=Perceived light intensity

σ= half saturation parameter

APPROACHES

PROPOSED ALGORITHM

1) Prediction of the Detail Loss Effect

PL= PLF (1-PL

D )

2) Enhancement of invisible pixels

JND Decomposition:

E= 2D tan (2.5 π/180)

Luminance Boosting and Compression:

Color Restoration:

Compensation for the Halo Effect

PERFORMANCE EVOLUTION

ABIE: Adaptive Backlight Image Enhancement

CBCS: Concurrent Brightness Contrast Scaling

TABS: Temporally aware backlight scaling

GD: Gradient Domain

Subjective Evaluation

Objective Evaluation

Visualization

CONCLUSION

Algorithm effectively

1. enhances the visibility of image dark region.

2. applies counter shading to eliminate halo effect.

3. enhances perceptual contrast of bright regions.

“A goal is not always meant to be reached. But often something to aim at”

-Bruce Lee