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Shift invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, e.g., the double-tree complex WI (DTCWT), have these useful properties. In this paper, we propose the use of a recently introduced implementation of such a WI, namely, the hyperanalytic WI (HWT), in association with filtering techniques already used with the discrete WI (DWT). The result is a very simple and fast image denoising algorithm. Some simulation results and comparisons prove the performance obtained using the new method. [ABSTRACT FROM AUTHOR]

Completely or partially disconnected electrodes are a fairly common occurrence in many electrical impedance tomography (EIT) clinical applications. Several factors can contribute to electrode disconnection: patient movement, perspiration, manipulations by clinical staff, and defective electrode leads or electronics. By corrupting several measurements, faulty electrodes introduce significant image artifacts. In order to properly manage faulty electrodes, it is necessary to: 1) account for invalid data in image reconstruction algorithms and 2) automatically detect faulty electrodes. This paper presents a two-part approach for real-time management of faulty electrodes based on the principle of voltage-current reciprocity. The first part allows accounting for faulty electrodes in EIT image reconstruction without apriori knowledge of which electrodes are at fault. The method properly weights each measurement according to its compliance with the principle of voltage-current reciprocity. Results show that the algorithm is able to automatically determine the valid portion of the data and use it to calculate high-quality images. The second part of the approach allows automatic real-time detection of at least one faulty electrode with 100% sensitivity and two faulty electrodes with 80% sensitivity enabling the clinical staff to fix the problem as soon as possible to minimize data loss. [ABSTRACT FROM AUTHOR]

Global and local histogram equalization (HE) proved to be effective techniques for contrast enhancement. Local HE allows to achieve high contrast enhancement rates but its high computational complexity limits its applicability in many resource constrained scenarios. On the contrary, global HE complexity is relatively low but enhancement rate is often unsatisfactory. Recently, an algorithm exploiting a low-pass filter (LPF) mask to obtain a partially overlapped sub-block HE effect (POSHE) has been presented. POSHE allows to produce the high local HE contrast with the simplicity of global HE. In this paper a generalization of POSHE approach exploiting cascaded multistep binomial filtering HE (CMBFHE) in order to get the same LPF mask is proposed. By relying on the same filter mask, contrast enhancement capability of CMBFHE is exactly the same of POSHE. Additionally, results show that a significant speedup with respect to the previous fastest method is achieved because of the efficient implementation of the filtering approach; moreover hardware implementations can be effectively designed. This makes CMBFHE a suitable solution in all those consumer electronics related environments including camcorders, digital cameras and video surveillance where on-the-fly local-like contrast enhancement is required. [ABSTRACT FROM AUTHOR]

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. In this paper, a new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger sub- blocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEAIMSEA. [ABSTRACT FROM AUTHOR]