Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

A Multiresolution Image Fusion Based on Principle Component Analysis

Abstract: A multiresolution image fusion method based on principle component analysis (PCA) is presented. A pairs of registered images are decomposed and of multi-resolution representation by wavelet transform, adaptive fusion weight value of the low frequency wavelet coefficients are resolved using PCA, the high frequency wavelet coefficients are fused by local wavelet energy maximum, then fused images is formed by inverse transforming and combining all wavelet coefficients, the proposed algorithm should keep the information of global structure and significant features from the input images. The experimental results show the proposed procedure work well in the precise feature, visibility and structure information of fused image.

Multiresolution Representation of Shapes Based on Cell Complexes

Abstract: This paper introduces a dimension-independent multiresolution model of a shape, called the Multi-Complex (MC), which is based on decomposition into cells. An MC describes a shape as an initial cell complex approximating it, plus a collection of generic modification patterns to such complex arranged according to a partial order. The partial order is essential to extract variable-resolution shape descriptions in real time. We show how existing multiresolution models reduce to special cases of MCs characterized by specific modification patterns. The MC acts as a unifying framework that is also useful for comparing and evaluating the expressive power of different approaches.

System for Multiresolution Analysis Assisted Reinforcement Learning Approach to Run-By-Run Control

Abstract: A new multiresolution analysis (wavelet) assisted reinforcement learning (RL) based control strategy that can effectively deal with both multiscale disturbances in processes and the lack of process models. The application of wavelet aided RL based controller represents a paradigm shift in the control of large scale stochastic dynamic systems of which the control problem is a subset. The control strategy is termed a WRL-RbR controller. The WRL-RbR controller is tested on a multiple-input-multiple-output (MIMO) Chemical Mechanical Planarization (CMP) process of wafer fabrication for which process model is available. Results show that the RL controller outperforms EWMA based controllers for low autocorrelation. The new controller also performs quite well for strongly autocorrelated processes for which the EWMA controllers are known to fail. Convergence analysis of the new breed of WRL-RbR controller is presented. Further enhancement of the controller to deal with model free processes and for inputs coming from spatially distributed environments are also addressed.

Identification and reconstruction of chaotic systems using multiresolution wavelet decompositions

Abstract: A new modelling framework for identifying and reconstructing chaotic systems is developed based on multiresolution wavelet decompositions. Qualitative model validation is used to compare the multiresolution wavelet models and it is shown that the dynamical features of chaotic systems can be captured by the identified models providing the wavelet basis functions are properly selected. Two basis selection algorithms, orthogonal least squares and a new matching pursuit orthogonal least squares, are considered and compared. Several examples are included to illustrate the results.

An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Abstract: Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey precise temporal and spatial information. Our proposed compression scheme is based upon multistage vector quantization of processed wave atoms representation of fingerprint images. Wave atoms expansion is processed using mathematical morphological operators to emphasize and retain significant coefficients for transmission. Quantized information is encoded using arithmetic entropy scheme. The proposed image compression standard outperforms other well established methods and achieves PSNR gain up to 8.07 dB in comparison to FBI's wavelet scalar quantization. Data mining, law enforcement, border security, and forensic applications can potentially benefit from our compression scheme.