This article presents a novel generalized approach to obtain rational (dyadic and integer) bi-orthogonal wavelet filter coefficients based on two conditions, namely maximally flatness and near-perfect reconstruction (PR) in half-band polynomial (HBP). An incremental-iterative approach is adopted to design the coefficients based on the proposed error equation in terms of the remainder polynomial (RP) of Lagrange HBP and the proposed HBP with maximum vanishing moments (VMs). In addition, VLSI architecture for the proposed wavelet filter banks (FBs) is designed and implemented on the Zedboard ZYNQ-7000 AP-SoC (Zynq FPGA from Xilinx) field-programmable gate array. It is found that the proposed rationalized wavelet FBs achieved significantly low digital hardware requirements with similar characteristics when compared to well-known rationalized existing bi-orthogonal wavelet FBs. The effectiveness of the designed wavelet FBs is verified in image compression and image retrieval on well-known publicly available databases. It is found that the designed rationalized bi-orthogonal wavelet FBs give better performances when compared to existing rationalized bi-orthogonal wavelet FBs.

Gabor wavelet is widely used in the analysis of texture features. It is found that Gabor wavelet filter bank (FB) requires infinite precision (due to irrational coefficients) to extract accurate directional textural features for which it needs numerous computations and external memory access. Due to this, there is a requirement for huge digital hardware, increased dynamic power dissipation, and increased processing time. In order to solve these issues, this article presents the first dyadic Gabor wavelet FB (DGWFB) based on the slight alteration in orientation parameter without disturbing the remaining Gabor wavelet parameters. In addition, this article introduces a separable VLSI architecture for the proposed DGWFB. The proposed VLSI architecture is implemented in field-programmable gate array (FPGA) through Xilinx Kintex 7 platform. It is observed that the proposed DGWFB has reduced processing time, dynamic power dissipation and digital hardware requirement significantly. The effectiveness of the designed DGWFB is evaluated in medical image retrieval application using three different publicly available medical databases namely NEMA, OASIS, and EXACT09. The proposed DGWFB achieved better performance as compared to existing Gabor wavelet FBs with significantly reduced digital hardware and processing time.

This paper presents a generalized novel design of a family of multiplier free orthogonal wavelet filter banks (FBs) with low complexity. In the proposed method, maximum number of zeros at$z=-1$is ensured for a desired length of analysis and synthesis filters. A new class of technique is proposed to obtain dyadic filter coefficients based on double shifting orthogonality property with allowable deviation from original filter coefficients. This is achieved by slightly altering perfect reconstruction (PR) condition to obtain dyadic filter coefficient. A general closed form expression is formulated using double shifting orthogonality property to reduce the error between original filter coefficients and proposed dyadic filter coefficients. The designed wavelet FB improved time-frequency product, Sobolev regularity and frequency selectivity (low energy in error) over well known existing wavelet FBs. In addition, a fixed point VLSI architecture is proposed for the designed family of orthogonal wavelet FBs. The suggested architecture is designed and implemented on Kintex7 FPGA board. The proposed family of orthogonal wavelet FBs is multiplier less with significantly reduced adders, shifters and dynamic power dissipation. The performance of the proposed wavelet FB is validated on two different applications namely image compression and medical image retrieval. The proposed FBs are tested on well known datasets such as CLASSIC, EPFL, RAISE, and FiveK for image compression and on three different medical image databases such as NEMA, OASIS, and EXACT09 for image retrieval.

Gabor wavelet is widely used in the analysis of texture feature. This study presents a novel feature descriptor based on the design of adaptive Gabor wavelet filter‐bank for medical image retrieval. The design of proposed Gabor wavelet provides flexibility to extract the dominant directional features from medical images. First, peaks in the spectrum of medical image are analysed to determine the dominant directions present in the image. With these dominant directions, a bank of Gabor‐filters is designed to extract the directional features effectively. Next, feature vector is derived by computing the energy and standard deviation from the Gabor filtered coefficients at a particular scale and orientation. The use of maximum likelihood estimation (MLE) is suggested to measure the similarity between the feature vectors of heterogeneous medical images. The performance of the proposed method is evaluated on three different publicly available databases namely NEMA, OASIS and EXACT09. The performance in terms of average retrieval precision (ARP), average retrieval rate (ARR) and computational time are compared with well‐known existing methods. It is observed from experimental results that the proposed approach achieved ARP of 85.32, 85.24 and 77% and ARR of 31.33, 14.05 and 23.78% for NEMA, OASIS and EXACT09 databases respectively.

This study presents a flexible directional filter bank (DFB) by tuning Hermite transform parameters for content‐based medical image retrieval (CBMIR). Two imperative Hermite transform parameters such as the scale of Gaussian kernel and the order of Hermite polynomial are tuned to adapt the flexible Hermite orthogonal basis to improve the retrieval performance of the CBMIR system. First, the tuned 1D Hermite filter transformed into 2D diamond shape filter by McClellan transformation and made it adequate for the directional decomposition of images using DFB structure. The dominant features of these directionally decomposed images are extracted by the rotation‐invariant local neighbourhood frequency pattern. The mean retrieval precision is used as a high‐level criterion to measure retrieval performance. The proposed method is assessed on three medical image databases: two for computed tomography and one for magnetic resonance imaging and achieved 99.41, 89.07, and 88.71% mean precision, respectively, when ten images are returned by the system.

This paper presents a novel approach for rationalization of irrational coefficients of filters designed using three-step lifting scheme. The existing three-step lifting schemes result in the irrational filter coefficients which require infinite precision for implementation. In this work, Euler-Frobenius halfband polynomials are designed to obtain the required kernels of three-step lifting structure. Next, the efficient rationalization of the designed filter banks (FBs) is proposed to reduce the arithmetic complexity. The proposed rational FBs preserve perfect reconstruction, near-orthogonality and regularity properties of wavelet FBs. These filters with rational coefficients are then used in image compression algorithms to compress the test images in well-known Classic, EPFL, RAISE, FiveK datasets and chromosome image datasets. The performance of the proposed rational FBs is compared with the existing rational FBs. It is observed that the performance of the proposed rational FBs is improved in terms of computational cost, encoding-decoding time and compression ratio as compared to existing rational FBs.

The design of wavelet filter‐bank in real continuum domain demands infinite precision to achieve perfect reconstruction (PR). The computations with irrational coefficients in orthogonal filters require a large amount of hardware that result in increased power dissipation, huge memory requirements and reduction in speed of operation. This paper presents a new approach to obtain a complete dyadic (low complexity) 6‐tap orthogonal symmetric wavelet filter‐bank (FB) with near perfect reconstruction. This is achieved by slightly altering the PR conditions to make orthogonal filters symmetric and to obtain complete dyadic filter coefficients. The proposed wavelet FB has reduced dynamic power dissipation and significantly reduced adder and shifter count. The VLSI architecture of the proposed FB is designed and implemented on Kintex‐7 FPGA. The architectural design is carried out using Verilog and functional simulation and synthesis are executed using Vivado 2016.4 software. The proposed FB dissipates 114mW dynamic power and requires only 5 adders and 3 shifters. The effectiveness of the proposed wavelet is verified in three different applications namely image compression, iris recognition system and orthogonal frequency division multiplexing (OFDM). The proposed wavelet FB gives comparable performance in image compression and attains superior performance in iris recognition system (feature extraction) and OFDM.

Abstract-This paper presents a shift, scale, and rotation-in- variant technique for iris feature-representation and fused postclassification at the decision-level to improve the accuracy and speed of the iris-recognition system. Most of the iris-recognition systems are still incapable for providing low false rejections due to a wide variety of artifacts and are computationally inefficient. In order to address these problems, effective and computationally efficient iris features are extracted based on a new class of triplet half-band filter bank (THFB). First, a new class of THFB is designed by using generalized half-band polynomial suitable for iris feature extraction. This THFB satisfies perfect reconstruction (PR) and provides linear phase, regularity, better frequency-selectivity, near-orthogonality, and good time-frequency localization. The uses of these properties are investigated to approximate iris features significantly. Second, a novel flexible k-out-of-n.A (Accept) postclassifier (any k-out-of-n-regions-Accept) is explored to achieve the robustness against possible intraclass iris variations. The proposed approach (THFB+ k-out-of-n.A) is capable of handling various artifacts, particularly segmentation error, eyelid/eyelashes occlusion, shadow of eyelids, head-tilt, and specular reflections during iris verification. Experimental results using UBIRIS, MMU1, CASIA-IrisV3, and IITD databases show the superiority of the proposed approach with some of the existing popular iris-recognition algorithms.