advanced exposure fusion using new boosting laplacian pyramid review 2.pptx
TRANSCRIPT
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Advanced Exposure Fusion Using New Boosting Laplacian Pyramid
Presentation by S.ABDULRAHAMAN(Roll No:14AT1D3801)
M.TECH(DECS)
Under the Guidance of
Mr.G.RAMARAOAssociate Professor
G.PULLAIAH COLLEGE OF ENGINEERING & TECHNOLOGY: KURNOOL
(An ISO 9001: 2008 Certified Institution)(Approved by AICTE, New Delhi. Affiliated to JNTU, Ananthapuramu)
Nandikotkur Road, Kurnool- 518002, A.P.
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Contents
• Introduction• Local exposure Weight • Global exposure Weight • JND-Based Saliency Weight• Block diagram• Advantages• Applications
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Introduction
New Exposure Fusion
Boosting Laplacian Pyramid
Exposure Fusion Algorithm
Guidance
Function
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New Exposure Fusion A new exposure fusion approach is proposed,
which is based on the novel boosting Laplacian pyramid and the hybrid exposure weight
HDR imaging techniques Gradient vectors Guidance methods to identify each pixel’s
contribution to the final fusion components The exposure difference with gradient
direction from the multiple exposure images
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Fig. 1(a) Input sequence. (b) Result (c) Our Result
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Boosting Laplacian Pyramid It is very useful to correctly select the salient
regions to boost, and the boosting level is controlled by the exposure quality measurement
Enhancement Images Exposure quality measurement. Base layer using the Gaussian pyramid is
given byR=
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Block Diagram
Input Images Local exposure weight
Global exposure weight
JND-Based Saliency Weight
Boosting guidance
Boosting function
Boosting guidance
Output Image
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Guidance Exposure regions and under-exposure or
over-exposure regions of the sequence should
be enhanced with different amplifying values
during the boosting process
Threshold operation σ where equals 0.01 in
our implementation is
=i(x, y)=i(x, y)+ i(x, y)
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Function The input signal is decomposed into the base and
detail signal using the Gaussian pyramid
Color information
Intensity-response
The multiple exposure fusion approach is often
used to recover the HDR characteristics of a given
image
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Local exposure Weight
Both under-exposure and over-exposure usually reveal some regions and also make other regions of the image invisible.
This exposure quality assessment Q(x, y) sets the lightest and darkest regions with zero values, while it assigns other regions with the values between zero and one.
(x, y)=rgb2gray((x, y))
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Global exposure Weight The local weight map does not utilize the
global relationship of measuring the exposure level between different exposure images.
A global exposure weight(x, y) to make a better exposure measurement by considering other exposure images from the sequence.
Finally, we multiply the weight map of each exposure image to obtain its final global exposure level of the input sequence.
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JND-Based Saliency Weight JND refers to the maximum distortion that the
human visual system does not perceive. Good color contrast In order to obtain more accurate JND
estimation, edge and no edge regions should be well distinguished
We can utilize a saliency weight map based function to estimate the level of boosting in our BLP
JND model helps us to represent the HVS sensitivity of observing an image.
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Advantages
Very efficient and work for color images.
Fusion work for different illumination changes.
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Applications
Extended depth-of-field.
Multi-sensor photography.
Non-photorealistic video.
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