K.A. Narayanankutty

Improved self-snake based anisotropic diffusion model for edge preserving image denoising using structure tensor

The performance of classifier algorithms used for predictive analytics highly dependent on quality of training data. This requirement demands the need for noise free data or images. The existing partial differential equation based diffusion models can remove noise present in an image but lacking in preserving thin lines, fine details and sharp corners. The classifier algorithms can able to make correct judgement to which class the image belongs to only if all edges are preserved properly during denoising process. To satisfy this requirement the authors proposed a new improved partial differential equation based diffusion algorithm for edge preserving image denoising. The proposed new anisotropic diffusion algorithm is an extension of self-snake diffusion filter which estimates edge and gradient directions as eigenvectors of a structure tensor matrix. The unique feature of this proposed anisotropic diffusion algorithm is diffusion rate at various parts of an image matches with the speed of level set flow. In the proposed algorithm an efficient edge indicator function dependent on the trace of the structure tensor matrix is used. The proposed model performs best in preserving thin lines, sharp corners and fine details since diffusion happens only along edges and diffusion is totally stopped across edges in this model. The additional edge-stopping term which is a vector dot product of derivative of an edge stopping function and derivative of an image computed along gradient and edge orthogonal directions is used in this model as shock filter which enables increased sharpness at all discontinuities. The performance of proposed diffusion algorithm is compared with other classical diffusion filters like conventional perona-malik diffusion, conventional self-snake diffusion methods.

Biorthogonal wavelet filters for compressed sensing ECG reconstruction

Abstract This paper elaborates the design details of a new set of bi orthogonal wavelet filters derived from double sided exponential splines. The designed wavelets are applied in compressed sensing (CS) scenario and results were quite promising. CS is a signal acquisition paradigm, which surpasses the traditional limit of Nyquist sampling. Increasing the reconstruction quality with minimum number of samples in CS is always challenging. We have addressed this challenging task of increasing the reconstruction quality within a minimum number of measurements in CS by developing this new set of biorthogonal wavelet filters. Biorthogonal wavelets have several advantages such as linear phase as compared to orthogonal wavelets. This wavelet which we prefer to call as dew1 (double exponential wavelet 1) is applied in CS based ECG reconstruction scenarios and experimented over 21 data records from MIT arrhythmia data base. A total of 950 experiments were conducted in three CS based methodologies for ECG reconstruction and the results were noted. Over all we were able to get nearly 30% improvement in the reconstruction quality. This paper elaborates the design of these bi orthogonal filters and its application in CS based ECG reconstruction scenario. Other than endorsing the results, we also aim to familiarize this newly designed wavelet so it can be further experimented in different domains.

A trick to improve PRD during compressed sensing ECG reconstruction

In the problem of compressed sensing (CS) successful reconstruction can be achieved by maintaining a low mutual coherence between the columns in the vector space. In this work, a way to increase the mutual incoherence is introduced. This is achieved by replacing certain matrix domain of the sparse random matrix, which is used as the measurement matrix with null space bases. For convenience, this can be replaced even by identity matrices. The result shows that there is a substantial improvement in Peak Root mean Square deviation (PRD). Many different alternatives have been tried out and relative PRD were plotted. Thresholding is generally adapted in CS in order to reduce the PRD values. It was found that without using thresholding technique, it is possible to obtain reduction in PRD values. The time algorithmic performance was also analyzed and found to be better.

Hardware Implementation of Edge detection on Hexagonal Sampled Image Grids

Increasing Processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications. Also, hexagonal representation has special computational features that are pertinent to the vision process. This paper describes Edge detection operation on hexagonally sampled images and its hardware implementation based on Cellular Logic Array Processing (CLAP) algorithm. This architecture builds up a virtual hexagonal grid system on the memory space of computer and processing algorithms can be implemented on such virtual spiral space, thereby decreasing the computational complexity. These operations were done on hexagonal sampled grid using MATLAB version 7 and the results were compared with rectangular sampled grid. MODELSIM and Quartus II software were used for analysis and synthesis. The performance was tested using Altera Cyclone II FPGA. It was observed from the results that there is a marginal improvement while processing with hexagonal sampled grid. Hardware utilization is found to be less for the image sampled on hexagonal grid compared with rectangular grid.