A. I. P 9512514 郭瓊蓮 922014 柯瑋明 922508 吳榮軒 Term Project

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A. I. P

9512514 郭瓊蓮 922014 柯瑋明 922508 吳榮軒

Term Project

• Implement this paper :“Two-scale Tone Management

for photographic Look,” Bae, Paris, and Durand.

• Apply the method to different kind of pictures.

• Add HDR technique.

Subject Review

Algorithm Review

model

input

base

detail

bilateral filter

high pass and local averaging textureness

texturenesstransfer

large-scaletransfer

Algorithm Reviewmodified base

modified detail

final output

constrained combination

postprocess

black-and-whiteoutput

Our works

Our input

Our model

Our works

Our detail

Our base

Our works

With edge preserving

Without edgepreserving

Our works

Our result

Author’s result

HDR

HDR

• Uncertainty.• Poisson equation.• Histogram matching.• Textureness.• Color channel.

Problems

• An old problem while using fast bilateral filter.

Uncertainty

Uncertainty

• Cost most time in our pipeline.

• Use Discrete Sine Transform to reduce time complexity.

• Easy to implement.

Poisson

• General Poisson Equation:– Ixx + Iyy = f

• For discrete version, we can rewrite the equation to matrix form:– TI + IT = F ,where T is a N*N triagonal matrix of

{1,-2,1}.

Poisson

• We define

SXS I

I2

1NS SS

SDTS

),...,diag( D

],...ss,[s S 2

1Nss

22N

j4sin where,s Ts

] 1N

N,...sin

1N

2sin,

1N[sins

2T

N21

N21

jkkTj

2jjjj

Tj

Poisson

Poisson

BSFS1N

2XDDX

SFSDXSSDXSS

SFSXSTSSSTSXS

FSXSTTSXS

FITTI

2

2222

22

• DX+XD=B is easy to solve

• Then we use I=SXS to get final answer.

Poisson

)/(bx

bxx

kjjkjk

jkkjkjkj

• In fact, SXS performs 2-D DST on X

• Implementation steps:– Perform 2-D DST on F– Divide the sum of the correspondin

g eigenvalue and a constant.– Perform 2-D DST again

Poisson

• The gray-value in log domain are always negative or zero.

• The range could be even wider if HDR added.

• The function implemented by MATLAB can only handle the interval from 0 to 1……

Hist-matching

Hist-matching

Inputdistribution

histogram

Hist-matching

Mask

distribution

histogram

Hist-matching

Outputdistribution

histogram

Hist-matching

Input

Output

Mask

Textureness

ρp = max( 0, ( T’p – T(B’)p ) / T(D)p )

T( I )p = 1/k * ∑ gσs( |p – q| ) gσr( |Ip - Iq| )|H|q

q∈|H|

k = ∑ gσs( |p – q| ) gσr( |Ip - Iq| ) q∈I

O = B’ + ρD

H is the high-pass version of the image.

Textureness

Input

Textureness

High frequency of H

Textureness

Absolute value of H

Textureness

T

Textureness

0 +

Textureness

• Which color channel could work best?

– RGB channel.• Process separately.• Process intensity only and then

interpolate the three channel.

– YUV channel.

Color Channel

Color Channel

Color Channel

Color Channel

Color Channel

More Images

Input Model

More Images

Input Output

More Images

Input

Model

More Images

Input Output

More Images

Input

Model

More Images

Input

Output

More Images

Input

Model

More Images

Input

Output

QuestionsThanks for your attention.