There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To evaluate the methods experimentally we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the methods based upon our framework with the ground truth as well as with prior methods.

There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.

Illumination plays an important role in the image capture process. Too little or too much energy in particular wavelengths can impact the scene appearance in a way that is difficult to manage by color constancy post processing methods. We use an adjustable multispectral flash to modify the spectral illumination of a scene. The flash is composed of a small number of narrowband lights, and the imaging system takes a sequence of images of the scene under each of those lights. Pixel data is used to estimate the spectral power distribution of the ambient light, and to adjust the flash spectrum either to match or to complement the ambient illuminant. The optimized flash spectrum can be used in subsequent captures, or a synthetic image can be computationally rendered from the available data. Under extreme illumination conditions images captured with the matching flash have no color cast, and the complementary flash produces more balanced colors. The proposed system also improves the quality of images captured in underwater environments.

Many multispectral imaging systems are computational in nature and require processing of raw data in order to obtain radiance spectra. In this paper, we derive a fast and scalable spectral estimation algorithm based on the Alternating Direction Method of Multipliers (ADMM). Using this approach we solve for the unknown surface spectral reflectance simultaneously for all pixels in the image. This global formulation allows us to incorporate spatial as well as spectral regularizers, such as total variation penalty or non-negativity. We show that the estimates derived with our solver are more accurate and more robust in the presence of noise.

This paper presents an underwater image formation model incorporating scatter and spectral absorption curves of pure water and three other particle classes; phytoplankton, non-algal particles and colored dissolved organic matter. We also describe an algorithm allowing to derive these depth dependent curves from a sequence of RGB images of a known target. Underwater images generated using the model closely resemble the experimental ones acquired in a variety of conditions. Similarly, the estimated absorption curves, and relative particle concentrations agree with common sense expectations.

We propose a color barcode framework for mobile phone applications by exploiting the spectral diversity afforded by the cyan (C), magenta (M), and yellow (Y) print colorant channels commonly used for color printing and the complementary red (R), green (G), and blue (B) channels, respectively, used for capturing color images. Specifically, we exploit this spectral diversity to realize a three-fold increase in the data rate by encoding independent data in the C, M, and Y print colorant channels and decoding the data from the complementary R, G, and B channels captured via a mobile phone camera. To mitigate the effect of cross-channel interference among the print-colorant and capture color channels, we develop an algorithm for interference cancellation based on a physically-motivated mathematical model for the print and capture processes. To estimate the model parameters required for cross-channel interference cancellation, we propose two alternative methodologies: a pilot block approach that uses suitable selections of colors for the synchronization blocks and an expectation maximization approach that estimates the parameters from regions encoding the data itself. We evaluate the performance of the proposed framework using specific implementations of the framework for two of the most commonly used barcodes in mobile applications, QR and Aztec codes. Experimental results show that the proposed framework successfully overcomes the impact of the color interference, providing a low bit error rate and a high decoding rate for each of the colorant channels when used with a corresponding error correction scheme.

This paper describes a new hardware architecture for barrel distortion correction in a color video stream. Such distortion is omnipresent in images acquired with a large field of view optics: wide-angle or fish-eye lenses. The designed platform is composed of a standard image sensor, a USB video class ASIC chip, a low cost FPGA and a SDRAM memory chip. Image processing algorithms are implemented in the FPGA, which is inserted between the sensor and the ASIC. The FPGA is connected to an external SDRAM in which a frame buffer is implemented. Barrel distortion is modeled using a polynominal relationship between corrected and distorted image spaces. Combinatorial logic circuit at the frame buffer output validates correct ordering of luminance and chrominance bytes in the data stream. The proposed design is capable of removing geometric distortion from 640 × 480 pixel images at the rate of 30 frames per second. Colors in reconstructed images are within ΔE = 2 from the originals in the CIELab color space.

This paper presents a hardware architecture for real time barrel distortion correction of YUV 4∶2∶2 encoded color images. In our solution we are presenting an alternative implementation of the correction engine which is based on multiplications rather than trigonometric transforms. The system makes minimal use of resources thus being a good candidate for embedding into a single chip. Proposed solution is implemented in a cost-effective Spartan 3 Field Programmable Gate Array (FPGA). Our architecture is capable of processing QQVGA images at the rate of 30 frames per second (fps). Qualitative and quantitative examination confirm correct preservation of color information in processed images.