Irtorgb

Introduction

In this project, we applied several machine learning algorithms like L3 (Local, Linear and Learned) and a neural network based model to find the appropriate mapping from NIR to RGB visible spectrum which human eyes are more sensitive to. We evaluate the results based on the CIELAB delta E metric for measuring difference in human perception level and the RMSE metric for quantitatively measuring pixels differences across image.

Background

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 color representation on NIR images would limit people’s understanding towards the image or even lead to wrong judgement. So colorizing the NIR grayscales 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 without interchannel correlation, which greatly reduces the effect of traditional color correction/transfer method. Moreover, since surface reflection in the NIR spectrum band is material dependent, some object might be missing from the NIR scenes due to their transparency to NIR. Therefore, IR colorization requires estimating not only chromiance, but also illuminance, which add a lot complexity to the problem.

Method

CNN Method

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

Dataset

A deep CNN model we built has over 2,376,723 trainable data, so a large amount of data is required to prevent overfitting, at which points the model perfectly fits the training data set but loses the ability to inference the spectrum transfer for a new IR image. Based on previous experiment on L3 which show single category training reaches a better result and a large demand of a large size data set, we then focused on collecting Urban Building dataset for CNN method. All the images were cropped into a 64 * 64 patches and then feed into the CNN model.

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CNN Model Description

A recent trend for CNNs is the usage of many convo- lution layers with small convolution kernels and relatively few pooling layers. This increases the total amount of non- linearities in the network. The model consists of nc convolution layers and np max-pooling layers. The pooling layers are distributed between the convolution layers so that each convolution layer block has the same amount of convolution layers. The activation function of the convolution layers is the ReLU function: ReLU(x) = max(0,x). Batch normalization is added after each relu.

Stochastic gradient descent using the backpropagation algorithm [30] is performed to minimize the mean squared error (MSE) between the pixel estimates F (p′i , Θ) of the ith normalized pixel p′i and the corresponding pixels qi of the mean filtered RGB image Tμ:

Appendix II

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