3 research outputs found
Denoising Method Based on Sparse Representation for WFT Signal
Affected by external noise and various nature disturbances, Wheel Force Transducer (WFT) signal may be completely submerged, and the sensitivity and the reliability of measurement can be strongly decreased. In this paper, a new wavelet packet denoising method based on sparse representation is proposed to remove the noises from WFT signal. In this method, the problem of recovering the noiseless signal is converted into an optimization problem of recovering the sparsity of their wavelet package coefficients, and the wavelet package coefficients of the noiseless signals can be obtained by the augmented Lagrange optimization method. Then the denoised WFT signal can be reconstructed by wavelet packet reconstruction. The experiments on simulation signal and WFT signal show that the proposed denoising method based on sparse representation is more effective for denoising WFT signal than the soft and hard threshold denoising methods
Statistical analysis and transfer of coarse-grain pictorial style
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 96-103).We show that image statistics can be used to analyze and transfer simple notions of pictorial style of paintings and photographs. We characterize the frequency content of pictorial styles, such as multi-scale, spatial variations, and anisotropy properties, using a multi-scale and oriented decomposition, the steerable pyramid. We show that the average of the absolute steerable coefficients as a function of scale characterizes simple notions of "look" or style. We extend this approach to account for image non-stationarity, that is, we capture and transfer the spatial variations of multi-scale content. In addition, we measure the standard deviation of the steerable coefficients across orientation, which characterizes image anisotropy and permits analysis and transfer of oriented structures. We focus on the statistical features that can be transferred. Since we couple analysis and transfer, our statistical model and transfer tools are consistent with the visual effect of pictorial styles. For this reason, our technique leads to more intuitive manipulation and interpolation of pictorial styles. In addition, our statistical model can be used to classify and retrieve images by style.by Soonmin Bae.S.M
INFORMATION THEORETIC CRITERIA FOR IMAGE QUALITY ASSESSMENT BASED ON NATURAL SCENE STATISTICS
Measurement of visual quality is crucial
for various image and video processing applications. It is widely
applied in image acquisition, media transmission, video compression,
image/video restoration, etc.
The goal of image quality assessment (QA) is to develop a computable
quality metric which is able to properly evaluate image quality. The
primary criterion is better QA consistency with human judgment.
Computational complexity and resource limitations are also concerns
in a successful QA design. Many methods have been proposed up to
now. At the beginning, quality measurements were directly taken from
simple distance measurements, which refer to mathematically signal
fidelity, such as mean squared error or Minkowsky distance. Lately,
QA was extended to color space and the Fourier domain in which
images are better represented. Some existing methods also consider
the adaptive ability of human vision. Unfortunately, the Video
Quality Experts Group indicated that none of the more sophisticated
metrics showed any great advantage over other existing metrics.
This thesis proposes a general approach to the QA problem by
evaluating image information entropy. An information theoretic model
for the human visual system is proposed and an information theoretic
solution is presented to derive the proper settings. The quality
metric is validated by five subjective databases from different
research labs. The key points for a successful quality metric are
investigated. During the testing, our quality metric exhibits
excellent consistency with the human judgments and compatibility
with different databases. Other than full reference quality
assessment metric, blind quality assessment metrics are also
proposed. In order to predict quality without a reference image, two
concepts are introduced which quantitatively describe the
inter-scale dependency under a multi-resolution framework. Based on
the success of the full reference quality metric, several blind
quality metrics are proposed for five different types of distortions
in the subjective databases. Our blind metrics outperform all
existing blind metrics and also are able to deal with some
distortions which have not been investigated