Spectral Image Processing Algorithm Research Articles

Mapping the Lithological Features and Ore-Controlling Structures Related to Ni–Cu Mineralization in the Eastern Tian Shan, NW China from ASTER Data

The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO2-rich fluids leaking to the participation of metallogenic processes.

Simulating the functionality of a digital camera pipeline

The goal of this paper is to simulate the functionality of a digital camera system. The simulations cover the conversion from light to numerical signal, color processing, and rendering. A spectral image processing algorithm is used to simulate the radiometric properties of a digital camera. In the algorithm, we take into consideration the spectral image and the transmittances of the light source, lenses, filters, and the quantum efficiency of a complementary metal-oxide semiconductor (CMOS) image sensor. The optical part is characterized by a multiple convolution between the different point spread functions optical components such as the Cooke triplet, the aperture, the light fall off, and the optical part of the CMOS sensor. The electrical part consists of the Bayer sampling, interpolation, dynamic range, and analog to digital conversion. The reconstruction of the noisy blurred image is performed by blending different light exposed images in order to reduce the noise. Then, the image is filtered, deconvoluted, and sharpened to eliminate the noise and blur. Next, we have the color processing and rendering blocks interpolation, white balancing, color correction, conversion from XYZ color space to LAB color space, and, then, into the RGB color space, the color saturation and contrast.

Simulation of the Photodetection in the PPS CMOS Sensors

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

A Matrix-Based Two-Dimensional Regularization Algorithm for Signal-to-Noise Ratio Enhancement of Multidimensional Spectral Data

We present a new spectral image processing algorithm, the "matrix maximum entropy method" (MxMEM), which offers efficient signal-to-noise ratio (SNR) enhancement of multidimensional spectral data. MxMEM is based upon two previous regularization methods that employ the maximum entropy concept. The first is a one-dimensional (1D) algorithm, which smoothes individual vectors, called the two-point maximum entropy method (TPMEM). The second is a two-dimensional (2D) form called 2D TPMEM, that smoothes images but processes them one vector at a time. MxMEM is a truly two dimensional image processing algorithm in that its "smoothing engine" performs two-dimensional processing in every iteration. We demonstrate that this matrix-based construction makes more effective use of two-dimensionally embedded information and thus confers significant advantages over other regularization approaches. In addition, we utilize the concept that individual related Raman spectra can be combined in a matrix to form an artificial Raman "image". We show that, when processed as an image, superior SNR enhancement is achieved compared to processing the same data by TPMEM one spectrum at a time.

A study about illumination and colors vision

In this paper we propose a software algorithm which can simulate the human eyes colors view. We present a spectral image processing algorithm, in which we use the color checker spectral image, the human eyes cones spectral sensitivity and the spectral power distribution for light sources. We use three spectrums of real lamps, made by EG&G PerkinElmer: sunlight 5500K, xenon flash and xenon CW, and three CIE light standard sources: D65, CIE A and F7. In order to validate the functionality of the algorithm we simulate the color checker picture hues difference, under different illuminations generate with the light spectrums presented in this paper.