Irtorgb

Introduction

Near-Infrared (NIR) images have broad application in remote sensing and surveillance for its capacity to segment images according to object’s material. Although NIR images made object detection an easier task, its monochrome nature is conflicted with human visual perception and thus might not be user friendly. Lack of color discrimination or wrong colors on NIR images would limit people’s understanding and even lead to wrong judgement. So colorizing the grayscale NIR images would be desired.

Colorization of NIR images is a difficult and challenging task since a single channel is mapped into a three dimensional space with unknown interchannel correlation, which greatly reduces the effectiveness of using traditional color correction/transfer method to solve this problem. Moreover, since surface reflection in the NIR spectrum band is material dependent, some objects might be missing from the NIR scenes due to their transparency to NIR. Therefore, different from grayscale image colorization which only estimates chrominance, IR colorization requires estimating not only the chrominance, but also the illuminance, which add a lot complexity to the problem.

In this project, we proposed several machine learning solutions like L3 (Local, Linear and Learned) and a neural network based model to find the appropriate mapping from NIR to RGB visible spectrum representation which human eyes are more sensitive to. The results are evaluated by CIELAB ∆E and MSE.

Method

CNN Method

Due to the great performance of CNN models in image classification tasks [1], we propose an integrated approach based on deep-learning techniques to perform a spectral transfer of NIR to RGB images. As inspired by Matthias et al. [2], a Convolutional Neural Network (CNN) is applied to directly estimate the RGB representation from a normalized NIR image. Then the colorized raw output of CNN model would go through an edge enhancement filter to transfer details from the high resolution IR input image and obtain better image quality.

Dataset

A deep CNN model might have thousands or even millions of trainable variables, so a large amount of data is required to prevent overfitting, where the model perfectly fits the training data set but loses the ability to inference the spectrum transfer on a new IR image. We use the RGB-NIR Scene Dataset [3] which consists of 477 images in 9 categories captured in RGB and Near-infrared (NIR).

Example NIR image of Urban Building

Example RGB image of Urban Building

Number of 64 * 64 Patches used in CNN Model
Data Type/ Data Use	Train	Test
Input IR (64 * 64)	7572	7572
Output RGB (64 * 64 * 3)	1044	1044

Based on promising result of single category training in L3, we used one category data that consists of 102 high resolution Urban Building images in our CNN model. All the images are split into 4/5 training data and 1/5 test data and then cropped into a 64 * 64 patches to feed into the CNN model.

CNN Architecture

CNN Model Architecture

Illustration of our CNN network architecture is given below. We use many convolution/deconvolution layers and relatively few pooling layers to increases the total amount of non linearities in the network. The-non linearities would help us learn complex mapping from IR to RGB. The activation function of each convolution layers is the Relu function: $Relu(x)=max(0,x)$, batch normalization is then followed to avoid overfitting.

In the training process, we use stochastic gradient descent to minimize the mean squared error (MSE) between the pixel estimates of normalized output (oRGB) and normalized ground truth RGB images(GT). MSE is defined as follows:

$MSE=\frac{1}{n}\sum _{i=1}^n(oRG{B}_i-\widehat{G{T}_i}{)}^2.$

Post Processing

The raw output of the CNN is blurry and has visible noise, which might be caused by inaccurate pixel-wise estimations. The subsampling property of the pooling layers combined with the correlation property of the convolution layers amplify this effect.[[4]] So post-processing is necessary to recover the lost details.

In the postprocessing step, the raw CNN output would go through guided filter[5], guided by high frequency details from the input IR image to produce the final colorized image. In the result section, you could see that compared to the raw output Eμ, object contours and edges are clearly visible.

Results

CNN

CNN model result

To compare our result with the ground truth RGB images, we used CIELAB ΔE metric to measure difference in human perception level and MSE metric to quantitatively measure pixels differences across image. Moreover, we also plot the distribution of true R and predict R to measure how close we are to the ground truth image. The training process lasted 1000 epoches in Nvdia GPU, using CUDA Toolkit 9.0 [6]. The following images gives the results for both train and test set colorization.

(1) Train set results

     MSE = 0.119

     CIELAB ΔE = 2.3265

blue: Output RGB values v.s. Target RGB of Example Train image; orange: true line

(2) Test set result

   MSE = 0.0134

   CIELAB ΔE = 4.8342

blue: Output RGB values v.s. Target RGB of Example Train image; orange: true line

We could see that training set with smaller MSE has best performance compared to the test set in the following three aspects:

a) the predicted RGB values locates more concentrated around the true line

b) the smaller CIELAB ΔE, which indicates smaller visual difference

c) visually more colorful

, Requires further training, Artificial object false colorized However, one thing to notice is that the raw output image of CNN is burry, it seems that we obtain color at the expense of resolution. In the next section, we gives the results of output images filtered by an edge enhance filter.

Guided Filter result

Guide Filter

CNN Final result

(1) Train set

(2) Test set

Appendix I

Github Page for this project: [7]

Appendix II

You can write math equations as follows: $y=x+5$

You can include images as follows (you will need to upload the image first using the toolbox on the left bar, using the "Upload file" link).

Example NIR image of Urban Building ]

Example NIR image of Urban Building

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City	NOC	Round 1	Round 2	Round 3
Rio de Janeiro	Template:BRA	26	46	66
Madrid	Template:ESP	28	29	32
Tokyo	Template:JPN	22	20	—
Chicago	Template:USA	18	—	—