Alias minimization of 1-D signals using DCT based learning
16 Aug 2010-pp 1121-1124
TL;DR: A learning based approach for alias minimization of 1-D signals by replacing the aliased DCT coefficients of the test signal with the best search from the training set.
Abstract: In this paper, we propose a learning based approach for alias minimization of 1-D signals. Given an under-sampled test speech signal and a training set consisting of several speech signals each of which are under-sampled as well as sampled at greater than Nyquist rate, we estimate the non-aliased frequencies for the test signal using the training set. The learning of non-aliased frequencies corresponds to estimating them using a training set. The test signal and each of the under-sampled training set signal are first interpolated to the size of The non-aliased signals. They are then divided into a number of segments and discrete cosine transform (DCT) is computed for each segment. Assuming that the lower frequencies are non-aliased and minimally distorted, we replace the aliased DCT coefficients of the test signal with the best search from the training set. The non-aliased test signal is then re-constructed by taking the inverse DCT. The comparison with the standard interpolation technique in terms of both subjective and quantitative analysis indicates better performance.
Citations
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01 Nov 2014
TL;DR: In this paper, a wavelet based frequency interpolation method is proposed to estimate the high frequency components of the narrowband test signal, and the reconstructed image has better resolution than the multiple bandwidth transducer approach.
Abstract: Photo-acoustic imaging has grown tremendously in the past few years, both in instrumentation and application. Due to its higher penetration, resolution and contrast it is gaining importance as a promising imaging modality. Commercially available transducers are typically bandlimited and thereby limit the frequency range of the received ultrasound signal. One approach is to use multiple bandwidth transducers but doing so increases the cost of the system. A wavelet based frequency interpolation method is proposed in this work. Low frequency band limited transducer is used in the experiments, and the missing high frequency components are estimated using an interpolation algorithm in the wavelet domain. A database of synthetic wideband photoacoustic data is constructed first. This database is then used to estimate the most likely high frequency components of the narrowband test signal. The approach provides better resolution at a low cost. It is shown that the reconstructed image has better resolution than multiple bandwidth transducer approach. This reduces the system complexity and along with compressive sensing techniques a hand held clinical photo-acoustic probe can be built.
3 citations
References
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Book•
01 Jul 1992
TL;DR: In this paper, a review of Discrete-Time Multi-Input Multi-Output (DIMO) and Linear Phase Perfect Reconstruction (QLP) QMF banks is presented.
Abstract: 1. Introduction 2. Review of Discrete-Time Systems 3. Review of Digital Filters 4. Fundamentals of Multirate Systems 5. Maximally Decimated Filter Banks 6. Paraunitary Perfect Reconstruction Filter Banks 7. Linear Phase Perfect Reconstruction QMF Banks 8. Cosine Modulated Filter Banks 9. Finite Word Length Effects 10. Multirate Filter Bank Theory and Related Topics 11. The Wavelet Transform and Relation to Multirate Filter Banks 12. Multidimensional Multirate Systems 13. Review of Discrete-Time Multi-Input Multi-Output LTI Systems 14. Paraunitary and Lossless Systems Appendices Bibliography Index
4,757 citations
"Alias minimization of 1-D signals u..." refers background in this paper
...It is important to note that our work differs from the work on multirate signal processing where the researchers attempt to design efficient decimators and interpolators for sampling rate conversion in digital domain and their objective is to change the sampling rate without introducing aliasing while working in digital domain [7]....
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TL;DR: The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts to present the technical review of various existing SR methodologies which are often employed.
Abstract: A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.
3,491 citations
"Alias minimization of 1-D signals u..." refers methods in this paper
...Our work is motivated by the work on image superresolution by the image processing community [1], [2], [3]....
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TL;DR: This work built on another training-based super- resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution that requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data.
Abstract: We call methods for achieving high-resolution enlargements of pixel-based images super-resolution algorithms. Many applications in graphics or image processing could benefit from such resolution independence, including image-based rendering (IBR), texture mapping, enlarging consumer photographs, and converting NTSC video content to high-definition television. We built on another training-based super-resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution. Our algorithm requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data. This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. We don't expect perfect resolution independence-even the polygon representation doesn't have that-but increasing the resolution independence of pixel-based representations is an important task for IBR.
2,576 citations
Additional excerpts
...Recently learning based approaches have been proposed for obtaining non-aliased and non-blur images using a database of training images at high resolution as well as at low resolution [4], [5], [6]....
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TL;DR: A method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition is introduced.
Abstract: Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion. >
580 citations
"Alias minimization of 1-D signals u..." refers methods in this paper
...Our work is motivated by the work on image superresolution by the image processing community [1], [2], [3]....
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