120 research outputs found
Regularity scalable image coding based on wavelet singularity detection
In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc
Anisotropic multiresolution analyses for deepfake detection
Generative Adversarial Networks (GANs) have paved the path towards entirely
new media generation capabilities at the forefront of image, video, and audio
synthesis. However, they can also be misused and abused to fabricate elaborate
lies, capable of stirring up the public debate. The threat posed by GANs has
sparked the need to discern between genuine content and fabricated one.
Previous studies have tackled this task by using classical machine learning
techniques, such as k-nearest neighbours and eigenfaces, which unfortunately
did not prove very effective. Subsequent methods have focused on leveraging on
frequency decompositions, i.e., discrete cosine transform, wavelets, and
wavelet packets, to preprocess the input features for classifiers. However,
existing approaches only rely on isotropic transformations. We argue that,
since GANs primarily utilize isotropic convolutions to generate their output,
they leave clear traces, their fingerprint, in the coefficient distribution on
sub-bands extracted by anisotropic transformations. We employ the fully
separable wavelet transform and multiwavelets to obtain the anisotropic
features to feed to standard CNN classifiers. Lastly, we find the fully
separable transform capable of improving the state-of-the-art
Rigorous free fermion entanglement renormalization from wavelet theory
We construct entanglement renormalization schemes which provably approximate
the ground states of non-interacting fermion nearest-neighbor hopping
Hamiltonians on the one-dimensional discrete line and the two-dimensional
square lattice. These schemes give hierarchical quantum circuits which build up
the states from unentangled degrees of freedom. The circuits are based on pairs
of discrete wavelet transforms which are approximately related by a
"half-shift": translation by half a unit cell. The presence of the Fermi
surface in the two-dimensional model requires a special kind of circuit
architecture to properly capture the entanglement in the ground state. We show
how the error in the approximation can be controlled without ever performing a
variational optimization.Comment: 15 pages, 10 figures, one theore
Standardised convolutional filtering for radiomics
The Image Biomarker Standardisation Initiative (IBSI) aims to improve
reproducibility of radiomics studies by standardising the computational process
of extracting image biomarkers (features) from images. We have previously
established reference values for 169 commonly used features, created a standard
radiomics image processing scheme, and developed reporting guidelines for
radiomic studies. However, several aspects are not standardised.
Here we present a preliminary version of a reference manual on the use of
convolutional image filters in radiomics. Filters, such as wavelets or
Laplacian of Gaussian filters, play an important part in emphasising specific
image characteristics such as edges and blobs. Features derived from filter
response maps have been found to be poorly reproducible. This reference manual
forms the basis of ongoing work on standardising convolutional filters in
radiomics, and will be updated as this work progresses.Comment: 62 pages. For additional information see https://theibsi.github.io
Image fusion using Wavelet Transform: A Review
An Image fusion is the development of amalgamating two or more image of common characteristic to form a single image which acquires all the essential features of original image Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc For fusing the image various techniques has been proposed by different author such as wavelet transform IHS and PCA based methods etc In this paper literature of the image fusion with wavelet transform is discussed with its merits and demerit
Fatty liver automatic diagnosis from ultrasound images
In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically
the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma
relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and
echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features.
The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity
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