Thermal Image Enhancement Using Convolutional Neural Network
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1 SEOUL Oct.7, 2016 Thermal Image Enhancement Using Convolutional Neural Network Visual Perception for Autonomous Driving During Day and Night Yukyung Choi Soonmin Hwang Namil Kim Jongchan Park In So Kweon
2 AGENDA Introduction to our Vision Introduction to Thermal Image Enhancement (TEN) The Period to Explore GPUs for Image Processing 2
3 Toward the Next Generation of Vision Technology Why do we use LWIR sensors? 3
4 4
5 KAIST Multi-spectral Recognition Dataset in day and night, IJRR under review. 5
6 KYLE BEAN - HISTORY OF MOBILE EVOLUTION, Motorola DynaTAC iphone 여주시립폰박물관 6
7 expensive heavy industrial cheap light commerciable Le Penseur 7
8 Our goal: Given a single modality, let s generate multispectral information. Time Invariance Spectral Image (LWIR) Transfer Visible-Spectral Context (Chromaticity, Depth, etc) (To be appear in IROS2016-Exhibition ) 8
9 What is the next generation vision sensor for use in any time of the day? Why do we enhance LWIR images? Visual information gives you a wider view than radars. - Angelova, IEEE Spectrum - 9
10 Thermal Infrared Camera Installation Dashboard BMW X5 (2014) Audi A8 (2014) Night Driving Trailer Mercedes Benz-S (2014) 10
11 Long wave infrared (LWIR) Visible (RGB) PM04 AM02 PM04 AM02 KAIST All-day dataset (FLIR A655sc, Point Grey Flea3) 8um~14um wavelength 400nm~700nm O light invariant X not enough texture/color enough But large diffraction distortion small small resolution large 11
12 Limitation #1: Resolution Image Enlargement (SR) Limitation #2: Diffraction Distortion (blur) Detail Enhancement (DDE) 320x240 (*LR) 640x480 ( HR) 640x480 (HR) 640x480 (Good Quality HR) *LR: low resolution, HR: high resolution Make HR image with LR image Improve HR image for better visibility and recognition performance 12
13 Image Enlargement (SR) Which type of image is useful in thermal image enhancing? Visible images? or Thermal images? or some other imaging spectra? Thermal image enhancing: when and where is it useful? Visibility? or Recognition performance? 13
14 (1) Which type of image is useful in thermal image enhancing? BLUR λ Visible um Short Wave IR um Middle Wave IR um Long Wave IR um Thermal Infrared Face Recognition A Biometric Identification Technique for Robust Security system, Refinements and New Ideas in Face Recognition. 14
15 (2) Thermal image enhancing: when and where is it useful? Input Bicubic Proposed GT Same or not? From Global-Local Face Upsampling Network, Arxiv (27 Apr 2016). 15
16 Image Enlargement : Architecture *LR Limitation #1: Resolution HR MSE (pixel loss) Feature Extraction Mapping Reconstruction (c in, c out,f,p) c in/out is the number of input/out channel, f is the size of filter, p is the size of padding. Bicubic : Interpolation Layer (preprocessing) Conv : Convolution Layer ReLU: Rectified Linear Unit Layer *LR: low resolution HR: high resolution 1) Shallow Network 2) MSE (pixel loss) 3) Bicubic Interpolation Thermal Image Enhancement Using Convolutional Neural Network, (To be appear in IROS2016) 16
![[RGB] RGB 91 Dataset](/docs-images/76/73342674/images/17-3.jpg "(gray channel)")
17 Image Enlargement : Data Train Data [RGB] RGB 91 Dataset (gray channel) Limitation #1: Resolution (1) Which type of image is useful in thermal image enhancing? Test Data [LWIR] Multimodal Stereo Dataset [MWIR] Thermal Stereo Dataset [LWIR] Multimodal Stereo Dataset Training parameter Pre-training: 64 64, 91 patches Fine-tuning: 36 36, stride 6, 118,211 patches No data augmentation The size of batch : 128, Learning rate: (decreased by a factor 10 at every 30 epochs until 60 epochs) 17
18 Image Enlargement : (1) Which type of image is useful in thermal image enhancing? Comparison of performance in Visible and MWIR Bicubic Gray-TENet MWIR-TENet PSNR(dB) Limitation #1: Resolution TENet x2 RGB MWIR 18
![RGB LWIR E-LWIR AM01:00 (d) (e) (f) [m] [m] Detections are](/docs-images/76/73342674/images/19-5.jpg "conducted on KAIST-RCV algorithm.")
![[1] [1] Multispectral Pedestrian Detection: Benchmark Dataset](/docs-images/76/73342674/images/19-7.jpg "and Baseline, CVPR2015 Trajectories are estimated by Andreas")
19 Limitation #1: Resolution Image Enlargement : (2) when and where is it useful? Far Pedestrian detection result (a) (b) Visual odometry result GT: GPS/IMU data (c) [m] RGB LWIR E-LWIR AM11:00 [m] RGB LWIR E-LWIR AM01:00 (d) (e) (f) [m] [m] Detections are conducted on KAIST-RCV algorithm. [1] [1] Multispectral Pedestrian Detection: Benchmark Dataset and Baseline, CVPR2015 Trajectories are estimated by Andreas s algorithm. [2] [2] StereoScan: Dense 3D Reconstruction in Real-time, IV
20 Detail Enhancement : Architecture Limitation #2: Diffraction Distortion *HR R HR Feature Extraction Mapping Reconstruction (c in, c out,f,p) c in/out is the number of input/out channel, f is the size of filter, p is the size of padding. Conv : Convolution Layer ReLU: Rectified Linear Unit Layer BatchNorm: Batch Normalization Layer *HR: input image HR: enhanced image R: Residual image Patent Pending* (To be appear in KINPEX2016) 20
![dataset [LWIR] dataset](/docs-images/76/73342674/images/21-9.jpg "Training parameter 64")
21 Detail Enhancement : Data Train Data [RGB] RGBT67 in KAIST all-day dataset (y channel) Limitation #2: Diffraction Distortion (1) Which type of image is useful in thermal image enhancing? Test Data [LWIR] RGBT67 in KAIST all-day dataset [LWIR] RGBT67 in KAIST all-day dataset Training parameter 64 64, stride 32, 136,800 patches Data Augmentation ( up-down flip, left-right flip ) The size of batch : 64, Learning rate:
22 Detail Enhancement : Data Limitation #2: Diffraction Distortion (1) Which type of image is useful in thermal image enhancing? Comparison of performance in Visible and LWIR *NIQE: Image distortion score [2] NIQE PSNR Input RGB Model LWIR Model [2] No Reference Image and Video Quality Assessment, SPL
23 RESULT VIDEO 23
24 It s time to explore GPUs for image processing 4th Industrial Revolution is just around the corner with GPUs 24
25 From NVIDIA 25
26 It s time to explore GPUs for image processing Image Enlargement Device Computatio nal Time Speed Up Frames per Sec CPU: i (3.30GHz) ms x GTX ms x Jetson TX ms x *test image : 320x240 (with caffe framework) I GTX 1080 Jetson TX1 26
27 Today s Summary Introduction of our vision Toward the Next Generation of Vision Technology Talk about Thermal Image Enhancement Network (TEN) Now let s explore GPUs for image processing! 27
28 SEOUL Oct.7, 2016 THANK YOU Thank you to all my coworkers Yukyung Choi Soonmin Hwang Namil Kim Jongchan Park In So Kweon
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