Color Restoration for Infrared Cutoff Filter Removed RGBN Multispectral Filter Array Image Sensor

Park, Chul Hee; Oh, Hyun Mook; Kang, Moon Gi

doi:10.5220/0005263600300037

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Furthermore, the tested scenario does not contain vegetation where the NIR influence plays a considerable role. This color correction in the constrained scenario was also considered in [ 17 ] (an extension of a former work presented in [ 18 ]), although the authors mentioned the presence of sunlight in the indoor tested scenes. In these works, the authors tackle the complex problem of restoring three channels (

and

), which are contaminated with an unknown quantity of NIR information.…”

Section: Related Workmentioning

confidence: 99%

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

Soria

Sappa

Hammoud

2018

Sensors

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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.

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and

), which are contaminated with an unknown quantity of NIR information.…”

Section: Related Workmentioning

confidence: 99%

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

Soria

Sappa

Hammoud

2018

Sensors

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“…Park et al [7], Kwon et al [8], and Park et al [9] developed a spectral decomposition based method that decomposes the NIR band into a color-correlated NIR band and color-independent NIR band to obtain each element of a 3 × 4 CCM. However, this method still relies on linear regression.…”

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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“…Han et al [2] combined four kinds of regression methods and nine kinds of nonlinear methods and comprehensively studied CCM-based RGBN color restoration methods. Park et al [8][9][10] proposed spectral decomposition-based methods that decompose the NIR channel into a color-relevant band and a color-irrelevant band and optimized CCM with statistical properties of different channels. Hertel et al [11] optimized a CCM by calculating the transfer matrix of the sensor response curve and target response curve.…”

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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Color Restoration for Infrared Cutoff Filter Removed RGBN Multispectral Filter Array Image Sensor

Cited by 6 publications

References 11 publications

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

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

Nonlinear Regression Color Correction Method for RGBN Cameras

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

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