| 1. |
- Gopakumar, Geethanjali, 1992-, et al.
(author)
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X-ray Induced Fragmentation of Protonated Cystine
- 2022
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In: Journal of Physical Chemistry A. - : American Chemical Society (ACS). - 1089-5639 .- 1520-5215. ; 126:9, s. 1496-1503
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Journal article (peer-reviewed)abstract
- We demonstrate site-specific X-ray induced fragmentation across the sulfur L-edge of protonated cystine, the dimer of the amino acid cysteine. Ion yield NEXAFS were performed in the gas phase using electrospray ionization (ESI) in combination with an ion trap. The interpretation of the sulfur L-edge NEXAFS spectrum is supported by Restricted Open-Shell Configuration Interaction (ROCIS) calculations. The fragmentation pathway of triply charged cystine ions was modeled by Molecular Dynamics (MD) simulations. We have deduced a possible pathway of fragmentation upon excitation and ionization of S 2p electrons. The disulfide bridge breaks for resonant excitation at lower photon energies but remains intact upon higher energy resonant excitation and upon ionization of S 2p. The larger fragments initially formed subsequently break into smaller fragments.
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| 2. |
- Hajisharif, Saghi, et al.
(author)
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Single Sensor Compressive Light Field Video Camera
- 2020
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In: Computer graphics forum (Print). - : Wiley-Blackwell. - 0167-7055 .- 1467-8659. ; 39:2, s. 463-474
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Journal article (peer-reviewed)abstract
- This paper presents a novel compressed sensing (CS) algorithm and camera design for light field video capture using a single sensor consumer camera module. Unlike microlens light field cameras which sacrifice spatial resolution to obtain angular information, our CS approach is designed for capturing light field videos with high angular, spatial, and temporal resolution. The compressive measurements required by CS are obtained using a random color-coded mask placed between the sensor and aperture planes. The convolution of the incoming light rays from different angles with the mask results in a single image on the sensor; hence, achieving a significant reduction on the required bandwidth for capturing light field videos. We propose to change the random pattern on the spectral mask between each consecutive frame in a video sequence and extracting spatioangular- spectral-temporal 6D patches. Our CS reconstruction algorithm for light field videos recovers each frame while taking into account the neighboring frames to achieve significantly higher reconstruction quality with reduced temporal incoherencies, as compared with previous methods. Moreover, a thorough analysis of various sensing models for compressive light field video acquisition is conducted to highlight the advantages of our method. The results show a clear advantage of our method for monochrome sensors, as well as sensors with color filter arrays.
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| 3. |
- Miandji, Ehsan, et al.
(author)
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Compressive HDR Light Field Imaging Using a Single Multi-ISO Sensor
- 2021
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In: IEEE Transactions on Computational Imaging. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2573-0436 .- 2333-9403. ; 7, s. 1369-1384
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Journal article (peer-reviewed)abstract
- In this paper, we propose a new design for single sensor compressive HDR light field cameras, combining multi-ISO photography with coded mask acquisition, placed in a compressive sensing framework. The proposed camera model is based on a main lens, a multi-ISO sensor and a coded mask located in the optical path between the main lens and the sensor that projects the coded spatio-angular information of the light field onto the 2D sensor. The model encompasses different acquisition scenarios with different ISO patterns and gains. Moreover, we assume that the sensor has a built-in color filter array (CFA), making our design more suitable for consumer-level cameras. We propose a reconstruction algorithm to jointly perform color demosaicing, light field angular information recovery, HDR reconstruction, and denoising from the multi-ISO measurements formed on the sensor. This is achieved by enabling the sparse representation of HDR light fields using an overcomplete HDR dictionary. We also provide two HDR light field data sets: one synthetic data set created using the Blender rendering software with two baselines, and a real light field data set created from the fusion of multi-exposure low dynamic range (LDR) images captured using a Lytro Illum light field camera. Experimental results show that, with a sampling rate as low as 2.67%, using two shots, our proposed method yields a higher light field reconstruction quality compared to the fusion of multiple LDR light fields captured with different exposures, and with the fusion of multiple LDR light fields captured with different ISO settings.
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| 4. |
- Tongbuasirilai, Tanaboon, et al.
(author)
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A Sparse Non-parametric BRDF Model
- 2022
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In: ACM Transactions on Graphics. - : ASSOC COMPUTING MACHINERY. - 0730-0301 .- 1557-7368. ; 41:5
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Journal article (peer-reviewed)abstract
- This paper presents a novel sparse non-parametric Bidirectional Reflectance Distribution Function (BRDF) model derived using a machine learning approach to represent the space of possible BRDFs using a set of multidimensional sub-spaces, or dictionaries. By training the dictionaries under a sparsity constraint, the model guarantees high-quality representations with minimal storage requirements and an inherent clustering of the BDRF-space. The model can be trained once and then reused to represent a wide variety of measured BRDFs. Moreover, the proposed method is flexible to incorporate new unobserved data sets, parameterizations, and transformations. In addition, we show that any two, or more, BRDFs can be smoothly interpolated in the coefficient space of the model rather than the significantly higher-dimensional BRDF space. The proposed sparse BRDF model is evaluated using the MERL, DTU, and RGL-EPFL BRDF databases. Experimental results show that the proposed approach results in about 9.75dB higher signal-to-noise ratio on average for rendered images as compared to current state-of-the-art models.
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