ICME2019 Tutorial: Intelligent Image Enhancement and Restoration - From Prior Driven Model to Advanced Deep Learning Part 4: retinex model based low light enhancement
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
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CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
PARTIAL PRODUCT ARRAY HEIGHT REDUCTION USING RADIX-16 FOR 64-BIT BOOTH MULTI...Hari M
PARTIAL PRODUCT ARRAY HEIGHT REDUCTION USING RADIX-16 FOR 64-BIT BOOTH MULTIPLIER:
Reduce the maximum height of the partial product columns to [n/4] for n = 64-bit unsigned
operand. This is in contrast to the conventional maximum height of [(n + 1)/4].
The multiplier algorithm is normally used for higher bit length applications and ordinary multiplier is good for lower order bits.
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Similar to Intelligent Image Enhancement and Restoration - From Prior Driven Model to Advanced Deep Learning Part 4: retinex model based low light enhancement
PARTIAL PRODUCT ARRAY HEIGHT REDUCTION USING RADIX-16 FOR 64-BIT BOOTH MULTI...Hari M
PARTIAL PRODUCT ARRAY HEIGHT REDUCTION USING RADIX-16 FOR 64-BIT BOOTH MULTIPLIER:
Reduce the maximum height of the partial product columns to [n/4] for n = 64-bit unsigned
operand. This is in contrast to the conventional maximum height of [(n + 1)/4].
The multiplier algorithm is normally used for higher bit length applications and ordinary multiplier is good for lower order bits.
Contribution to the optimization of energy withdrawn from a PV panel using an...saad motahhir
Considering the high initial capital cost of photovoltaic (PV) panels and their low conversion efficiency, it is imperative to operate the PV system at the maximum power point (MPP). In this context, our goal in this thesis is to develop and improve the PV system, by contributing to the optimization of energy withdrawn from PV panel using an embedded system. For this purpose, in order to simulate and test MPPT algorithm, the model of the PV panel should be first studied in accordance with the real behavior of the PV panel. Therefore, the single diode model of the PV panel is introduced in Matlab/Simulink and PSIM. Moreover, for the first time, the PV panel model is developed in Proteus; an experimental test bench was built to validate the developed model. On the other hand, this work proposes a modified incremental conductance (INC) algorithm to improve the MPP tracker (MPPT) capability for PV system when the irradiation is suddenly modified. Three modifications are made in the INC algorithm, which are described as follows: (1) A check to identify the increase in irradiation and make a correct decision. (2) Eliminate the all-division computations in the INC algorithm and make the algorithm structure simpler allowing the algorithm to be easily implemented by a low-cost embedded system. (3) A modified variable step INC algorithm is used, which can reduce the steady-state oscillations and improve the tracking speed under sudden irradiance variation. The first modification is simulated using PSIM through “Software in the Loop” test and the results show that the modified algorithm provides an accurate response to a sudden variation of solar irradiation with an efficiency of 98.8 %. The second modification is simulated using the PV panel model proposed in Proteus. For verification, a hardware test bench is implemented by using Arduino Uno board in which the low-cost Atmega328 microcontroller is integrated. This has led to a low-cost PV system with an efficiency of 98.5 %. The third modification is developed following the techniques employed in the automotive and aeronautical embedded system. This is done by following the V-cycle development process, which means that our controller will be validated using “Model in the Loop/Software in the Loop/Processor in the Loop” tests. In this sense, integrating the MPPT embedded system in the automotive or the aeronautical area will be possible. It should be mentioned that Matlab/Simulink is used for MIL/SIL/PIL tests, thus STM32F4 board is used for PIL test. On the other side, if minimizing the cost of the PV system is not important than guarantying a very high level of robustness and efficiency, it is required to use a more powerful method. Therefore in this thesis, we design and implement MPPT based on Kalman Filter. The expected outcome of this proposal is an efficient MPPT method which presents a very high level of robustness, reliability and accuracy. The obtained results clearly highlight the superiority of
Introduction to Wavelet Transform and Two Stage Image DE noising Using Princi...ijsrd.com
In past two decades there are various techniques are developed to support variety of image processing applications. The applications of image processing include medical, satellite, space, transmission and storage, radar and sonar etc. But noise in image effect all applications. So it is necessary to remove noise from image. There are various methods and techniques are there to remove noise from images. Wavelet transform (WT) has been proved to be effective in noise removal but this have some problems that is overcome by PCA method. This paper presents an efficient image de-noising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). This method provides better preservation of image local structures. In this method a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected from the local window by using block matching based LPG. In image de-noising, a compromise has to be found between noise reduction and preserving significant image details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensions. It is a standard technique commonly used for data reduction in statistical pattern recognition and signal processing. This paper proposes a de-noising technique by using a new statistical approach, principal component analysis with local pixel grouping (LPG). This procedure is iterated second time to further improve the de-noising performance, and the noise level is adaptively adjusted in the second stage.
Design and implementation of a Neural Network based image compression engine as part of Final Year Project by Jesu Joseph and Shibu Menon at Nanyang Technological University. The project won the best possible grade and excellent accolades from the research center.
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An Efficient DSP Based Implementation of a Fast Convolution Approach with non...a3labdsp
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Batch normalization: Accelerating Deep Network Training by Reducing Internal ...ssuser6a46522
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APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
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The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
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Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
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8. STRUCT Group08
Representative Work
Related Works
Histogram
Equalization
Enhance the contrast
Over-enhancement / under-enhancement
Amplify the noise
Before HE After HE
9. STRUCT Group09
Representative Work
Related Works
Histogram
Equalization
Dehazing Method
Inverted low-light images vs. hazy images
Invert dehaze invert again
Require an additional denoising process
Low-Light Inversion Dehazing Result
10. STRUCT Group010
Representative Work
Related Works
Histogram
Equalization
Dehazing Method Retinex Model
Retinex-based methods
Retinex decomposition
Generate results
S R L
1
enhanceS R L
Gamma
Correction
Low-Light Image
Enhanced Image
Illumination (L)
Reflectance (R)
12. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13] Bright-pass filter preserve naturalness
12
13. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
Bright-pass filter preserve naturalness
13
14. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
LIME[TIP17]
Bright-pass filter preserve naturalness
14
15. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
LIME[TIP17]
Estimate L and R simultaneously
Bright-pass filter preserve naturalness
15
16. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
LIME[TIP17]
Estimate L and R simultaneously
PIE[TIP15]
Bright-pass filter preserve naturalness
16
17. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
LIME[TIP17]
Estimate L and R simultaneously
PIE[TIP15]
SRIE[CVPR16]
Bright-pass filter preserve naturalness
18. STRUCT GroupRelated Works
Retinex model based methods
Retinex Model
Estimate L, R = I/L, refine R
NPE[TIP13]
Fusion based[SP16]
LIME[TIP17]
Estimate L and R simultaneously
PIE[TIP15]
SRIE[CVPR16]
CEID[TIP17]
Bright-pass filter preserve naturalness
18
20. STRUCT Group020
Representative Work
Related Works
Histogram
Equalization
Dehazing Method
Retinex Model
Learning-Based
Method
Low-Light Image Dataset
Regression Model OutputInput
Low-Light Image Dataset
…
24. STRUCT Group24 Robust Retinex Model for Low Light Enhancement
Robust Retinex Model for Low Light Enhancement
Structure-Revealing Low-Light Image Enhancement Via Robust
Retinex Model
Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo
TIP 2018
I R L I R L N
Input image Retinex Model Robust Retinex Model
25. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
An additional noise term
Drawbacks of conventional model
Focus on the estimation of L
Got noisy reflectance for
Calculate both R and L iteratively
Introduce noise to illumination by minimizing
25
' /R R N L
2
|| ||FR L S
26. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
An additional noise term
Priors for low-light images
Illumination map piece-wise smoothed
Reflectance map low contrast
Noise map relatively low intensity
26
27. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
27
28. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
Data fidelity term
28
29. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
Input image Illumination map
Illumination constraint
29
30. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
Reflectance constraint
30
31. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
Without constraint With constraint
Reflectance constraint
31
32. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The optimization function
Noise constraint
Input image w/o constraint w/ constraint Noise map
32
33. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The solution
Importing an auxiliary variable T
Augmented Lagrange equation
33
34. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The solution
Sub-problem R
Sub-problem L
Sub-problem N
34
35. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Robust Retinex model
The solution
Sub-problem T
Updating auxiliary variables
35
36. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
PIE[TIP15] SRIE[CVPR16] Proposed
Input image LIME[TIP17]
NPE[TIP13]
36
37. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
PIE[TIP15] SRIE[CVPR16] Proposed
Input image LIME[TIP17]
NPE[TIP13]
37
38. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
Input image LIME[TIP17]
NPE[TIP13] PIE[TIP15] SRIE[CVPR16] ProposedHE
38
39. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
PIE[TIP15]
SRIE[CVPR16] Proposed
Input image LIME[TIP17]
NPE[TIP13]
39
40. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
PIE[TIP15]
SRIE[CVPR16] Proposed
Input image LIME[TIP17]
NPE[TIP13]
40
41. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
Input image LIME[TIP17]
NPE[TIP13] PIE[TIP15] SRIE[CVPR16] ProposedHE
41
42. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
Objective criteria
The lower, the better quality The higher, the better quality
42
51. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
ProposedInput image Fu[ICIP14]
Underwater image enhancement
51
52. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
Smoky/hazy image enhancement
ProposedInput image He[CVPR09]
52
53. STRUCT GroupRobust Retinex Model for Low Light Enhancement
Experimental Results
Enhancement of images taken under dusty weather
ProposedInput image Fu[MMSP14]
53
57. STRUCT GroupSequential Decomposition for Low Light Enhancement
Architecture
In RGB SpaceS
Illumination Estimation
Reflectance Estimation
L
R
S’
𝐿
W G
Restrict Matrices
L’
57
58. STRUCT GroupSequential Decomposition for Low Light Enhancement
Illumination Estimation
2
1
ˆarg min || || || ||F
L
L L L
Low-Light Image Initial Illumination Estimated Illumination
Estimate illumination independent from reflectance
58
59. STRUCT GroupSequential Decomposition for Low Light Enhancement
Reflectance Estimation
Estimate reflectance based on refined illumination
and original image
2 2 2
arg min || / || || || || ||F F F
R
R S L W R R G
Low-Light Image S / L Estimated Reflectance
59
60. STRUCT GroupSequential Decomposition for Low Light Enhancement
Reflectance Estimation
Use weighted matrices to restrict noise
ˆ| |/ ˆ(1 )
0, if | |ˆ
, otherwise
S
G e S
S
S
S
1
| |
W
S eps
G W
60
61. STRUCT GroupSequential Decomposition for Low Light Enhancement
Solution
Estimate the illumination map
Approximate:
Rewrite the original problem:
Simplify:
2
1
x {h,v}
( ( ))
|| || .
ˆ| L( ) |
d
d d
L x
L
x eps
2
2
x {h,v}
( ( ))ˆarg min || || .
ˆ| L( ) |
d
F
L d d
L x
L L
x eps
2 2
x {h,v}
ˆarg min || || ( ) ( ( )) .F d d
L d
L L A x L x
61
62. STRUCT GroupSequential Decomposition for Low Light Enhancement
Solution
Estimate the illumination map
Estimate the reflectance map
{h,v}
ˆDiag( )T
d d d
d
I D a D l l
{h,v} {h,v}
{h,v}
Diag( )
/
T T
d d d d d
d d
T
d d
d
I D w D D D r
s l D g
62
63. STRUCT GroupSequential Decomposition for Low Light Enhancement
Experimental Results
Experimental settings
All experiments are performed in MATLAB R2017a with
4G RAM and Intel Core i5-4210H CPU @2.90GHz.
In our experiment the parameters α, β and γ are
empirically set as 0.007, 0.001 and 0.016.
In our experiment the parameters ε and σ are set to 10
and λ is set to 6.
63
64. STRUCT GroupSequential Decomposition for Low Light Enhancement
Experimental Results
LIME (top panel) and ours (bottom panel)
Input images Illumination Reflectance Result images Details
64
65. STRUCT GroupSequential Decomposition for Low Light Enhancement
Experimental Results
LIME (top panel) and ours (bottom panel)
Input images Illumination Reflectance Result images Details
65
74. STRUCT GroupSequential Decomposition for Low Light Enhancement
Summary
Based on a refined Retinex model
Noise-removal and Enhancing
Sequential decomposition
74
78. STRUCT GroupDeep Retinex Decomposition
Hand-Crafted Retinex
Not easy to be adaptive to complex and varying low-light
conditions
Under-enhancementOver-enhancement Boundary artifacts
78
79. STRUCT GroupDeep Retinex Decomposition
Direct End-to-End Learning
Difficulties in directly recovering normal-light images
Inherent ambiguity
Low-Light Image Dataset
Regression Model OutputInput
Low-Light Image Dataset
…
79
80. STRUCT GroupDeep Retinex Decomposition
Direct End-to-End Learning
Regression to mean
Over-smoothed results with degraded contrast
Over-smoothness Degraded contrast
80
81. STRUCT GroupDeep Retinex Decomposition
Our Solution: Retinex-Net
Retinex Theory + Deep Learning
Low-Light Image Dataset
OutputInput
Retinex
Decomposition
Adjusted
Decomposition
Regression
Model
…
81
83. STRUCT GroupDeep Retinex Decomposition
Architecture of Retinex-Net
, ,
recon ij i j j
i low normal j low normal
L R I S
ir low normalL R R
, ,
exp( )is j i g j i
i low normal j h v
L I R
Reconstruction Loss
Constant Reflectance Loss
Illumination Smoothness Loss
83
84. STRUCT GroupDeep Retinex Decomposition
Architecture of Retinex-Net
, ,
exp( )is j i g j i
i low normal j h v
L I R
,
is i
i low normal
L I
Illumination Smoothness Loss
84
89. STRUCT GroupDeep Retinex Decomposition
Real Photography Pairs
LOw Light paired dataset (LOL)
1000 low/normal-light image pairs
500 are collected by changing only exposure time and ISO
Various scenes, e.g., houses, clubs, streets, etc.
89
90. STRUCT GroupDeep Retinex Decomposition
Dataset
Synthetic Pairs from Raw Images
1000 raw images from RAISE[Dang-Nguyen 2015]
Fitting the histogram of Y channel in YCbCr to real low-light images
Online available: https://daooshee.github.io/BMVC2018website/
90
92. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by LIME I by LIME
Normal-Light Image R by LIME I by LIME
92
93. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by NPE I by NPE
Normal-Light Image R by NPE I by NPE
93
94. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by Retinex-Net I by Retinex-Net
Normal-Light Image R by Retinex-Net I by Retinex-Net
94
95. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by LIME I by LIME
Normal-Light Image R by LIME I by LIME
95
96. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by NPE I by NPE
Normal-Light Image R by NPE I by NPE
96
97. STRUCT GroupDeep Retinex Decomposition
Decomposition
Low-Light Image R by NPE I by NPE
Normal-Light Image R by NPE I by NPE
97