Sensitivity calibration of digital colour cameras for auroral imaging

CVNet: confidence voting convolutional neural network for camera spectral sensitivity estimation

Spectral sensitivity, as one of the most important parameters of a digital camera, is playing a key role in many computer vision applications. In this paper, a confidence voting convolutional neural network (CVNet) is proposed to rebuild the spectral sensitivity function, modeled as the sum of weighted basis functions. By evaluating useful information supplied by different image segments, disparate confidence is calculated to automatically learn basis functions' weights, only using one image captured by the object camera. Three types of basis functions are made up and employed in the network, including Fourier basis function (FBF), singular value decomposition basis function (SVDBF), and radial basis function (RBF). Results show that the accuracy of the proposed method with FBF, SVDBF, and RBF is 97.92%, 98.69%, and 99.01%, respectively. We provide theory for network design, build a dataset, demonstrate training process, and present experimental results with high precision. Without bulky benchtop setups and strict experimental limitations, this proposed simple and effective method could be an alternative in the future for spectral sensitivity function estimation.

Compressive recovery of smartphone RGB spectral sensitivity functions

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.

ASI aurora search: an attempt of intelligent image processing for circular fisheye lens

The circular fisheye lens exhibits an approximately 180° angular field-of-view (FOV), which is much larger than that of an ordinary lens. Thus, images captured with a circular fisheye lens are distributed non-uniformly with spherical deformation. Along with the fast development of deep neural networks for normal images, how to apply it to achieve intelligent image processing for a circular fisheye lens is a new task of significant importance. In this paper, we take the aurora images captured with all-sky-imagers (ASI) as a typical example. By analyzing the imaging principle of ASI and the magnetic characteristics of the aurora, a deformed region division (DRD) scheme is proposed to replace the region proposals network (RPN) in the advanced mask regional convolutional neural network (Mask R-CNN) framework. Thus, each image can be regarded as a "bag" of deformed regions represented with CNN features. After clustering all CNN features to generate a vocabulary, each deformed region is quantified to its nearest center for indexing. On the stage of an online search, a similarity score is computed by measuring the distances between regions in the query image and all regions in the data set, and the image with the highest value is outputted as the top rank search result. Experimental results show that the proposed method greatly improves the search accuracy and efficiency, demonstrating that it is a valuable attempt of intelligent image processing for circular fisheye lenses.

Estimation of spectral distribution of sky radiance using a commercial digital camera

Methods for estimating spectral distribution of sky radiance from images captured by a digital camera and for accurately estimating spectral responses of the camera are proposed. Spectral distribution of sky radiance is represented as a polynomial of the wavelength, with coefficients obtained from digital RGB counts by linear transformation. The spectral distribution of radiance as measured is consistent with that obtained by spectrometer and radiative transfer simulation for wavelengths of 430-680 nm, with standard deviation below 1%. Preliminary applications suggest this method is useful for detecting clouds and studying the relation between irradiance at the ground and cloud distribution.

High performance gel imaging with a commercial single lens reflex camera

A high performance gel imaging system was constructed using a digital single lens reflex camera with epi-illumination to image 19 × 23 cm agarose gels with up to 10,000 DNA bands each. It was found to give equivalent performance to a laser scanner in this high throughput DNA fingerprinting application using the fluorophore SYBR Green(®). The specificity and sensitivity of the imager and scanner were within 1% using the same band identification software. Low and high cost color filters were also compared and it was found that with care, good results could be obtained with inexpensive dyed acrylic filters in combination with more costly dielectric interference filters, but that very poor combinations were also possible. Methods for determining resolution, dynamic range, and optical efficiency for imagers are also proposed to facilitate comparison between systems.

White-light Sagnac interferometer for snapshot multispectral imaging

The theoretical and experimental demonstration of a multispectral Sagnac interferometer (MSI) is presented. The MSI was created by including two multiple-order blazed diffraction gratings in both arms of a standard polarization Sagnac interferometer (PSI). By introducing these high-order diffractive structures, unique spectral passbands can be amplitude modulated onto coincident carrier frequencies. Extraction of the modulated multispectral images, corresponding to each passband, is accomplished within the Fourier domain. This yields a unique multispectral sensor capable of imaging all the passbands in a single snapshot. First, the theoretical operating principles of a PSI are discussed to provide a context for the MSI. This is followed by the theoretical and experimental development of the MSI, which is an extension of a dispersion-compensated PSI. Indoor and outdoor testing and validation of the MSI are performed by observing vegetation, demonstrating the ability of our experimental setup to detect four distinct spectral passbands.

The absolute sensitivity of digital colour cameras

A new and improved method to obtain the average spectral pixel responsivity and the quantum efficiency of Digital Single Lens Reflex (DSLR) cameras is outlined. Two semi-professional cameras, the Nikon D300 and the Canon 40D, are evaluated. The cameras red, green and blue pixel responsivities and quantum efficiency are retrieved by illuminating an integrating sphere with a wavelength tunable monochromator. 31 intensity calibrated monochromatic spectral lines from 4000 to 7000 A, with a bandpass of approximately 12 A, were used as a library to solve the main equations of observation for the cameras. Both cameras have peak sensitivity in the blue and minimum sensitivity in the red. The Canon 40D has blue and green channel sensitivity close to the Nikon D300. The Canon red channel has half the sensitivity of the Nikon camera.