5,890 research outputs found
Exact Histogram Specification Optimized for Structural Similarity
An exact histogram specification (EHS) method modifies its input image to
have a specified histogram. Applications of EHS include image (contrast)
enhancement (e.g., by histogram equalization) and histogram watermarking.
Performing EHS on an image, however, reduces its visual quality. Starting from
the output of a generic EHS method, we maximize the structural similarity index
(SSIM) between the original image (before EHS) and the result of EHS
iteratively. Essential in this process is the computationally simple and
accurate formula we derive for SSIM gradient. As it is based on gradient
ascent, the proposed EHS always converges. Experimental results confirm that
while obtaining the histogram exactly as specified, the proposed method
invariably outperforms the existing methods in terms of visual quality of the
result. The computational complexity of the proposed method is shown to be of
the same order as that of the existing methods.
Index terms: histogram modification, histogram equalization, optimization for
perceptual visual quality, structural similarity gradient ascent, histogram
watermarking, contrast enhancement
Robust equalization of multichannel acoustic systems
In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved
by equalizing the channels from the source to microphones. Equalization systems can
be computed using estimates of multichannel acoustic impulse responses. However,
the estimates obtained from system identification always include errors; the fact that
an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this
thesis is to propose and investigate robust equalization methods for multichannel
acoustic systems in the presence of system identification errors.
Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems
obtained from these methods are very sensitive to system identification errors. A
study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least-
squares method is proposed. Channel shortening in connection with the multiple-
input/output inverse theorem and the relaxed multichannel least-squares method is
discussed.
Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is
proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization
method exploring the use of error models is presented, which incorporates system
identification error models in the weighted multichannel least-squares formulation
An Algorithm on Generalized Un Sharp Masking for Sharpness and Contrast of an Exploratory Data Model
In the applications like medical radiography enhancing movie features and observing the planets it is necessary to enhance the contrast and sharpness of an image. The model proposes a generalized unsharp masking algorithm using the exploratory data model as a unified framework. The proposed algorithm is designed as to solve simultaneously enhancing contrast and sharpness by means of individual treatment of the model component and the residual, reducing the halo effect by means of an edge-preserving filter, solving the out of range problem by means of log ratio and tangent operations. Here is a new system called the tangent system which is based upon a specific bargeman divergence. Experimental results show that the proposed algorithm is able to significantly improve the contrast and sharpness of an image. Using this algorithm user can adjust the two parameters the contrast and sharpness to have desired output
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function
This paper addresses the problem of speech separation and enhancement from
multichannel convolutive and noisy mixtures, \emph{assuming known mixing
filters}. We propose to perform the speech separation and enhancement task in
the short-time Fourier transform domain, using the convolutive transfer
function (CTF) approximation. Compared to time-domain filters, CTF has much
less taps, consequently it has less near-common zeros among channels and less
computational complexity. The work proposes three speech-source recovery
methods, namely: i) the multichannel inverse filtering method, i.e. the
multiple input/output inverse theorem (MINT), is exploited in the CTF domain,
and for the multi-source case, ii) a beamforming-like multichannel inverse
filtering method applying single source MINT and using power minimization,
which is suitable whenever the source CTFs are not all known, and iii) a
constrained Lasso method, where the sources are recovered by minimizing the
-norm to impose their spectral sparsity, with the constraint that the
-norm fitting cost, between the microphone signals and the mixing model
involving the unknown source signals, is less than a tolerance. The noise can
be reduced by setting a tolerance onto the noise power. Experiments under
various acoustic conditions are carried out to evaluate the three proposed
methods. The comparison between them as well as with the baseline methods is
presented.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language
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