Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images

Soria, Xavier Gimeno; Sappa, Ángel D.; Hammoud, Riad I.

doi:10.3390/s18072059

Cited by 14 publications

(6 citation statements)

References 40 publications

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“…In multi-spectral red, green, blue, near-infrared (RGB-NIR) images, the visible (RGB) and near-infrared (NIR) spectral bands are captured simultaneously by a 4-sensor line scan camera [1]. The RGB spectral bands are in the visible range (400-700 nm), whereas the NIR spectral band is beyond the visible range (700-1100 nm).…”

Section: Introductionmentioning

confidence: 99%

Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN

et al. 2019

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As 4-sensor line scan camera technology has matured, red (R), green (G), blue (B), and near-infrared (RGB-NIR) datasets have begun to appear in large numbers. The RGB-NIR data contain the rich color features of the RGB image and the sharp edge features of the NIR image. At present, in many studies, the RGB-NIR data are input directly into the processing algorithms for calculation of the 4D data; in these cases, redundant information is included, and the high correlation between the bands results in an inability to fully exploit the characteristics of the RGB-NIR data. In this paper, we propose a double-channel convolutional neural network (CNN) algorithm that takes into account the strong correlation between the R, G, and B bands in aerial images and the weaker correlation between the NIR band and the R, G, and B bands. First, the features of the RGB and NIR bands are calculated in two different CNN networks, and subsequently, feature fusion is performed in the fully connected layer. This is followed by the classification. By combining the two neural networks of RGB-CNN and NIR-CNN, the respective characteristics of the RGB-NIR data are fully exploited. INDEX TERMS Multi-spectral, CNN, RGB-NIR, double-channel CNN.

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Section: Introductionmentioning

confidence: 99%

Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN

et al. 2019

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“…The proposed method was compared with ENDENet and CDNet [17] on the SSMID dataset. The color restoration results of the subset of SSMID called OMSIV are shown in Fig.15.…”

Section: Ssmid Dataset Color Restoration Resultsmentioning

confidence: 99%

“…They also proposed an RGBN camera color restoration method using the same simple network structure with a different optimizer and activation function. Soria et al [17] proposed ENDENet and CDNet for the color restoration task. Gharbi et al [18] proposed HDR-Net, which combines a bilateral grid and local affine color transforms.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

Han

Jin

et al. 2020

IEEE Photonics J.

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RGBN cameras that can capture visible light and near-infrared (NIR) light simultaneously produce better color image quality in low-light-level conditions. However, these RGBN cameras introduce additional color bias caused by the mixing of visible information and NIR information. The color correction matrix model widely used in current commercial color digital cameras cannot handle the complicated mapping function between biased color and ground truth color. Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. In order to achieve satisfactory training results, large amounts of data must be captured manually, even when data augmentation techniques are applied, requiring significant time and effort. Hence, a data generation method for training pairs that are consistent with target RGBN camera parameters, based on an open access RGB-NIR dataset, is proposed. The proposed method is verified by training an RGBN camera color restoration CNN model with generated data. The results show that the CNN model trained with the generated data can achieve satisfactory RGBN color restoration performance with different RGBN sensors.

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“…Subsequently, Soria et al [14] used different optimizers and activation functions with another simple network structure to correct colors. They later trained the model with a convolutional neural network [15].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Regression Color Correction Method for RGBN Cameras

Han

Jin

et al. 2020

IEEE Access

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With the development of multi-spectral imaging techniques, many new multi-spectral imaging devices have been developed in recent years. Red-green-blue and near-infrared (RGBN) cameras are widely used because they capture visible light and near-infrared light simultaneously, but they inevitably introduce color desaturation. Because there is clear multicollinearity among the RGBN channels, the ordinary least squares regression (OLSR) color correction method performs poorly. To solve color bias and multicollinearity, an RGBN camera color correction pipeline is proposed. A large number of nonlinear regression color correction methods that consist of combinations of four regression methods and nine nonlinear transforms are evaluated in this study. The results show that the proposed OLSR-based compound transform color correction method and partial least-squares regression (PLSR) based Gaussian-core transform color correction method yield better color correction results and are more robust. These approaches reduce the multicollinearity of the RGBN camera channels and will be a valuable reference in the development of RGBN imaging applications. INDEX TERMS Color correction, multicollinearity near-infrared, nonlinear regression, RGBN camera.

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Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images

Cited by 14 publications

References 40 publications

Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN

Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN

Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

Nonlinear Regression Color Correction Method for RGBN Cameras

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