Triplet excited electronic state switching induced by hydrogen bonding: A transient absorption spectroscopy and time-dependent DFT study

The solvent plays a decisive role in the photochemistry and photophysics of aromatic ketones. Xanthone (XT) is one such aromatic ketone and its triplet-triplet (T-T) absorption spectra show intriguing solvatochromic behavior. Also, the reactivity of XT towards H-atom abstraction shows an unprecedented decrease in protic solvents relative to aprotic solvents. Therefore, a comprehensive solvatochromic analysis of the triplet-triplet absorption spectra of XT was carried out in conjunction with time dependent density functional theory using the ad hoc explicit solvent model approach. A detailed solvatochromic analysis of the T-T absorption bands of XT suggests that the hydrogen bonding interactions are different in the corresponding triplet excited states. Furthermore, the contributions of non-specific and hydrogen bonding interactions towards differential solvation of the triplet states in protic solvents were found to be of equal magnitude. The frontier molecular orbital and electron density difference analysis of the T-1 and T-2 states of XT indicates that the charge redistribution in these states leads to intermolecular hydrogen bond strengthening and weakening, respectively, relative to the S-0 state. This is further supported by the vertical excitation energy calculations of the XT-methanol supra-molecular complex. The intermolecular hydrogen bonding potential energy curves obtained for this complex in the S-0, T-1, and T-2 states support the model. In summary, we propose that the different hydrogen bonding mechanisms exhibited by the two lowest triplet excited states of XT result in a decreasing role of the n pi* triplet state, and are thus responsible for its reduced reactivity towards H-atom abstraction in protic solvents. (C) 2016 AIP Publishing LLC.

IMAGE DENOISING USING OPTIMALLY WEIGHTED BILATERAL FILTERS: A SURE AND FAST APPROACH

The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.