Design Of Image Filters Research Articles

Design of cognitive image filters for suppression of noise level in medical images

Currently, medical image processing is used for the diagnosis of various diseases. But current technologies for medical image processing needs brighter light of research, since medical images are obtained from the various devices are affected by the different noises which in turn affects the accuracy of diagnosis/detection. Hence we propose the intelligent and cognitive image filters which works on the principle of adaptive selection theorem in which filters are selected based on the powerful fuzzy rule engine. The proposed filters are then compared with the previous filters and results obtained are more pivotal when compared with other filters.

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming.

In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.

Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications

In this paper, we present an energy-efficient architecture of the Canny edge detector for advanced mobile vision applications. Three key techniques for reducing computational complexity of the Canny edge detector are presented. First, by exploiting the rank characteristic of the convolution kernel of Gaussian smoothing and Sobel gradient filters, common computations are identified and shared in the image filter design to reduce the number of additions and multiplications. For the gradient magnitude/direction computation, only three directions of neighboring pixels are considered to reduce computation energy with minor degradation on conformance performance (CP). For the adaptive threshold selections, an interesting observation is that the mean values of gradient magnitudes show small variations depending on the classified block types. Thus, the threshold selection process can be simplified as multiplying the mean value of the local block with predecided constants. The proposed low complexity Canny edge detector has been implemented using both field-programmable gate arrays (FPGAs) and a 65-nm standard-cell library. The FPGA implementation with Xilinx Virtex-V (XC5VSX240T) shows that our edge detector achieves 48% of area and 73% of execution time savings over the conventional architecture without seriously sacrificing the detection performance. The proposed edge detector implemented with 65-nm standard-cell library can easily support real-time ultrahigh definition video data processing (50 frames/s) with the power consumption of 5.48 mW (108.84 $\mu \text{J}$ /frame).

An advanced computing in fuzzy rule-based preprocessing design of image filters’ system for removing impulse noises

In this study, advanced computing in fuzzy rule-based preprocessing classifier is used to categorize five distribution types that are identified as noise by noise detection from the service of next era cloud-empowered computing and techniques. The proposed method extracts useful local information from the corrupted image that is supported by filter processing and resulted in more image details to preserve. The fuzzy rule-based preprocessing classifier goes through a four-phase detection procedure to determine the condition of central pixels for the local image window by using the similarity between the neighboring pixels. A filters’ system is set up with our proposed fuzzy rule-based preprocessing classifier with several effective filters to verify its performance. Simulation results are compared with other individual filters by objective numerical measurements and subjectively visual inspection to indicate that our proposed method performs significantly better in terms of noise suppression and detail preservation.

Design of image enhancement filters using a novel parallel particle swarm optimisation algorithm

Designing image enhancement filters with arbitrary frequency response subject to stability constraints is a complex multidimensional optimisation problem. In this paper a novel parallel particle swarm optimisation algorithm (PPSO) is proposed and applied to the design of infinite impulse response image filters. The proposed PPSO consists of two phases: in the first phase, the particle swarm in the classical PSO algorithm is divided into subpopulations that evolve on separate cores of a multi-core machine. Best solutions from each sub population are then interchanged between cores. In the second phase a local search using Nelder-Mead simplex is done to refine the solution. Classical PSO is used for global exploration to explore multiple local minima whereas Nelder-Mead helps refine the solution computed by the PSO. The PPSO outperformed other global optimisation algorithms in terms of the mean square error between the ideal and designed filter frequency responses and CPU usage.

An image filter based on primary frequency analysis to improve the bit error rate in holographic data storage systems

The primary frequency analysis method is proposed to design an image filter for applications in holographic data storage systems that use a Nyquist aperture. We designed the filter by analyzing the frequency of the data patterns, requiring neither the point spread function of the system nor iteration. The component frequencies that constitute the data pattern were calculated, and the modulation transfer function (MTF) determines that the primary frequency components was boosted. This filter boosts the primary frequency region where the modulation transfer function (MTF) <0.5 to reduce the bit error rate (BER). The performance of the designed image filter was verified by both simulation and experimentation. The proposed system exhibited an improvement in BER from 0.0215 to 0.0070 for a Nyquist factor of 1.1 and from 0.0061 to 0.0017 for a Nyquist factor of 1.2. These results are expected to contribute to the design of holographic data image filters.

Low-Level Flexible Architecture with Hybrid Reconfiguration for Evolvable Hardware

Field-programmable gate arrays (FPGAs) can be considered to be the most popular and successful platform for evolvable hardware. They allow one to establish and later reconfigure candidate solutions. Recent work in the field of evolvable hardware includes the use of virtual and native reconfigurations. Virtual reconfiguration is based on the change of functionality by hardware components implemented on top of FPGA resources. Native reconfiguration changes the FPGA resources directly by means provided by the FPGA manufacturer. Both of these approaches have their disadvantages. The virtual reconfiguration is characterized by lower maximal operational frequency of the resulting solutions, and the native reconfiguration is slower. In this work, a hybrid approach is used merging the advantages while limiting the disadvantages of the virtual and native reconfigurations. The main contribution is the new low-level architecture for evolvable hardware in the new Zynq-7000 all-programmable system-on-chip. The proposed architecture offers high flexibility in comparison with other evolvable hardware systems by considering direct modification of the reconfigurable resources. The impact of the higher reconfiguration time of the native approach is limited by the dense placement of the proposed reconfigurable processing elements. These processing elements also ensure fast evaluation of candidate solutions. The proposed architecture is evaluated by evolutionary design of switching image filters and edge detectors. The experimental results demonstrate advantages over the previous approaches considering the time required for evolution, area overhead, and flexibility.

Hardware-in-the-loop Simulation based Evolutionary Design of Image Filter

An evolutionary design method of image filter based on hardware-in-the-loop (HIL) simulation is proposed. An image filter evolvable system based on System Generator was designed. Experimental results and analyses demonstrate that the optimal evolved filter outperforms traditional filters both on performance and implementation costs. In comparison with the existing evolutionary design methods of image filter, the proposed strategy brings higher computational ability and more flexibility. Evolutionary algorithm of the evolvable system can be synthesized into hardware which provided a basis for the follow-up research to achieve online adaptive filtering. DOI: http://dx.doi.org/10.5755/j01.eee.20.7.3375

Iterative design method for an image filter to improve the bit error rate in holographic data storage systems

An image filter design method is proposed to improve the bit error rate (BER) in holographic data storage (HDS). In previous image restoration methods for HDS, a highly precise point spread function (PSF) was used as the deconvolution filter, so performance was strongly dependent on the accuracy of the PSF. In the proposed method, in contrast, the image filter is designed without using PSF information, due to the similar spatial frequency distributions of HDS data pages. A simple iteration algorithm was developed to obtain an optimized image-filter shape, and the performance of the designed image filter was verified by both simulation and experimentation. The experimental results demonstrated that the BER improved sixfold and greater than tenfold for a Nyquist factor of 1.1 and 1.2, respectively. Additionally, an improvement of >30 % in storage density was projected using the proposed filter, compared with conventional HDS systems.

Fault-tolerant image filter design using particle swarm optimization

This article describes a mixed constrained image filter design with fault tolerance using particle swarm optimization (PSO) on a reconfigurable processing array. There may be some faulty configurable logic blocks (CLBs) in a reconfigurable processing array. The proposed method with PSO autonomously synthesizes a filter fitted to the reconfigurable device with some faults in order to optimize the complexity and power of the circuit, and the signal delay in both the CLBs and the wires. An image filter for noise reduction is experimentally synthesized to verify the validity of our method. By evolution, the quality of the optimized image filter on a reconfigurable device with a few faults is almost same as that on a device with no faults.

Mixed Constrained Image Filter Design for Salt-and-Pepper Noise Reduction Using Genetic Algorithm

This paper describes mixed constrained image filter design for noise reduction using a Genetic Algorithm (GA) with parameterized uniform crossover. The proposed method with GA autonomously synthesizes a filter suitable for the reconfigurable processing array, evaluating the complexity, power and signal delay in both Configurable Logic Blocks (CLBs) and wires. An image filter for noise reduction is experimentally synthesized to verify the validity of the proposed method. By the evolution, the quality of the optimized image filter is better than that reported in other papers.

Erratum to: Mixed constrained image filter design using particle swarm optimization

Erratum to: Mixed constrained image filter design using particle swarm optimization

Mixed constrained image filter design using particle swarm optimization

This article describes an evolutionary image filter design for noise reduction using particle swarm optimization (PSO), where mixed constraints on the circuit complexity, power, and signal delay are optimized. First, the evaluated values of correctness, complexity, power, and signal delay are introduced to the fitness function. Then PSO autonomously synthesizes a filter. To verify the validity of our method, an image filter for noise reduction was synthesized. The performance of the resultant filter by PSO was similar to that of a genetic algorithm (GA), but the running time of PSO is 10% shorter than that of GA.

Linear filtering of images based on properties of vision

The design of linear image filters based on properties of human visual perception has been shown to require the minimization of criterion functions in both the spatial and frequency domains. We extend this approach to continuous filters of infinite support. For lowpass filters, this leads to the concept of an ideal lowpass image filter that provides a response that is superior perceptually to that of the classical ideal lowpass filter.

Advances in mathematical models for image processing

Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several image Processing problems, including image restoration, smoothing, enhancement, data compression, spectral estimation, and filter design, are discussed and examples given.