Image Adaptive Denoising Method Based on Compressive Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 993-996
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng ◽  
Sun Li
Keyword(s):  
Genetic Algorithm ◽  
Image Quality ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Experimental Results ◽  
New Method ◽  
Denoising Method ◽  
Edge Information ◽  
Fast Speed ◽  
Adaptive Denoising

We present a new adaptive denosing method using compressive sensing (CS) and genetic algorithm (GA). We use Regularized Orthogonal Matching Pursuit (ROMP) to remove the noise of image. ROMP algorithm has the advantage of correct performance, stability and fast speed. In order to obtain the optimal denoising effect, we determine the values of the parameters of ROMP by GA. Experimental results show that the proposed method can remove the noise of image effectively. Compared with other traditional methods, the new method retains the most abundant edge information and important details of the image. Therefore, our method has optimal image quality and a good performance on PSNR.

Incremental Hyperplane Partitioning for Classification

2013 ◽  
Vol 4 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Tao Yang ◽  
Sheng-Uei Guan ◽  
Jinghao Song ◽  
Binge Zheng ◽  
Mengying Cao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Equations ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
New Method ◽  
Complex Pattern ◽  
Classification Problems ◽  
Encoding Scheme ◽  
Incremental Approach ◽  
Higher Dimensional

The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

Optimizing Fourier Series Based Gaits for Modular Robots Using an Evolutionary Algorithm

2013 ◽  
Author(s):  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Genetic Algorithm ◽  
Fourier Series ◽  
Evolutionary Algorithm ◽  
Periodic Motion ◽  
Degrees Of Freedom ◽  
Series Representation ◽  
Robotic System ◽  
Experimental Results ◽  
New Method ◽  
Modular Robots

A new method of controlling and optimizing robotic gaits for a modular robotic system is presented in this paper. A robotic gait is implemented on a robotic system consisting of three Mobot modules for a total of twelve degrees of freedom using a Fourier series representation for the periodic motion of each joint. The gait implementation allows robotic modules to perform synchronized gaits with little or no communication with each other making it scalable to increasing numbers of modules. The coefficients of the Fourier series are optimized by a genetic algorithm to find gaits which move the robot cluster quickly and efficiently across flat terrain. Simulated and experimental results show that the optimized gaits can have over twice as much speed as randomly generated gaits.

Shape Modification of NURBS Curves Using Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1450-1454
Author(s):  
Yan Hong Li ◽  
Guo Wang Mu ◽  
Zeng Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Experimental Results ◽  
New Method ◽  
Shape Modification ◽  
Nurbs Curve ◽  
Nurbs Curves

In this paper, we propose a new method for shape modification of NURBS curves. For a given NURBS curve, we modify its one or more weights so that the curve passes through the point specified in advance. We convert this into an optimization problem and solve it by genetic algorithm. The experimental results show the feasibility and validity of our method.

Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

2014 ◽  
Vol 998-999 ◽  
pp. 1033-1036
Author(s):  
Jian Wang ◽  
Xiao Hu Duan ◽  
Yan Li ◽  
Peng Bai
Keyword(s):  
Genetic Algorithm ◽  
Effective Properties ◽  
High Accuracy ◽  
Experimental Results ◽  
New Method ◽  
Oil Analysis ◽  
Maintenance Costs ◽  
Vector Machines ◽  
Aero Engine ◽  
Genetic Algorithm Model

Diagnosis of engine fault is critical in reducing maintenance costs. A new method which incorporates hybrid relative vector machines and genetic algorithm (RVM-GA) was proposed to predict aero engine fault based on data of the spectrometric oil analysis. Experimental results show that it has a high accuracy and effective properties.

PROJECTIVE VOLUME RENDERING BY EXCLUDING OCCLUDED VOXELS

2005 ◽  
Vol 05 (02) ◽  
pp. 413-431
Author(s):  
WENCHENG WANG ◽  
HANQIU SUN ◽  
ENHUA WU
Keyword(s):  
Image Quality ◽  
Volume Rendering ◽  
Experimental Results ◽  
New Method ◽  
Ray Casting ◽  
Exclusion Problem ◽  
Data Volume ◽  
Projection Approach ◽  
Set Up

In volume rendering, an important issue in acceleration is to reduce the calculations for occluded voxels. Although this issue has been addressed in the ray casting approach, it is difficult to apply the idea to the projection approach due to uncertain termination conditions. In this paper, we propose a new method to effectively address the exclusion problem in the projection approach, so the rendering process can be accelerated without impairing the rendered image quality. In the rendering process, this new method employs the dynamic screen technique to manage the pixels whose accumulated opacity has not reached 1.0. A ray-cast link at each pixel is set up to record the rendered voxels for the corresponding ray cast from the pixel to intersect. According to the rendered voxels covering the pixels whose accumulated opacity value is below 1.0, visible voxels are selected to render from front to back by the neighboring relationship between the rendered voxels and the voxels to be rendered. Thus, the occluded voxels are dynamically excluded from the loading and rendering processes accurately. Our proposed method can be in general applied to both parallel and perspective projections, using regular and irregular volume datasets. Our experimental results showed that the proposed method can significantly accelerate volume rendering if the data volume has a high percentage of occluded voxels. This method can also perform fairly efficiently for the expensive shading calculations if requested in volume rendering.

Image Interpolation Based on Level-Sets Motion with Artificial Speed Field

2013 ◽  
Vol 718-720 ◽  
pp. 2131-2135
Author(s):  
Ming Gang Jing ◽  
Ji Tao Wu
Keyword(s):  
Image Quality ◽  
Level Sets ◽  
Interpolation Method ◽  
Image Interpolation ◽  
Experimental Results ◽  
New Method ◽  
Subjective Image Quality ◽  
Magnification Factors ◽  
Shock Filter

This paper presents a novel image interpolation method based on level-sets motion (LSM). The proposed method computes the speed field of level-sets adaptively, according to which the contours in images evolve at proper speeds. Thus it can produce images with less jagged edges stably and fast. To suppress blurring artifacts, the shock filter is used to get sharper edges in images. And the new method can interpolate images with arbitrary magnification factors (MFs). Experimental results have verified the effectiveness of the proposed method in terms of both objective and subjective image quality.

Cooperative Obstacle Avoidance of Multiple Robotic Fishes Based on Grids Method

2014 ◽  
Vol 536-537 ◽  
pp. 929-933
Author(s):  
Yu Lian Jiang
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Optimal Path ◽  
Experimental Results ◽  
New Method ◽  
Local Minima ◽  
New Approach ◽  
Serial Number

To resolve the problem of obstacle avoidance and path planning of multiple robotic fishes, this paper proposes a new approach which uses collaboration mechanism based on grids method. The proposed approach splits the workspace of those robotic fishes using grids method, identifies each grid with serial number, designs the cooperative mechanism to avoid collision among these fishes, and plans the path for each fish. This method was applied in the obstacle avoidance competition of multiple robotic fishes which happening in a field with obstacle in it. The experimental results show the new method is more effective, can get more optimal path, and avoid the local minima issue which arises frequently in the A-star algorithm and genetic algorithm. It significantly improves the ability of the multiple robotic fishes system on the aspect of path planning and coordination.

scholarly journals STEREO MATCHING ALGORITHM BASED ON ILLUMINATION CONTROL TO IMPROVE THE ACCURACY

2016 ◽  
Vol 35 (1) ◽  
pp. 39 ◽  
Author(s):  
Rostam Affendi Hamzah ◽  
Haidi Ibrahim ◽  
Anwar Hasni Abu Hassan
Keyword(s):  
Image Quality ◽  
Stereo Vision ◽  
Stereo Matching ◽  
State Of The Art ◽  
Experimental Results ◽  
New Method ◽  
Absolute Difference ◽  
Matching Algorithm ◽  
Disparity Maps

This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

scholarly journals MULTIWAVELET ADAPTIVE DENOISING METHOD BASED ON GENETIC ALGORITHM

2009 ◽  
Vol 28 (1) ◽  
pp. 77-80 ◽  
Author(s):  
Lin ZHANG ◽  
Zhi-Jun FANG ◽  
Sheng-Qian WANG ◽  
Fan YANG ◽  
Guo-Dong LIU
Keyword(s):  
Genetic Algorithm ◽  
Denoising Method ◽  
Adaptive Denoising

Image Reconstruction Based on Compressive Sensing via CoSaMP

2014 ◽  
Vol 631-632 ◽  
pp. 436-440 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Curvelet Transform ◽  
Stopping Rule ◽  
Compressive Sampling ◽  
New Method ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Number Of Iterations

Compressive Sampling Matching Pursuit (CoSaMP) is a new iterative recovery algorithm which has splendid theoretical guarantees for convergence and delivers the same guarantees as the best optimization-based approaches. In this paper, we propose a new signal recovery framework which combines CoSaMP and Curvelet transform for better performance. In classic CoSaMP, the number of iterations is fixed. We discuss a new stopping rule to halting the algorithm in this paper. In addition, the choice of several adjustable parameters in algorithm such as the number of measurements and the sparse level of the signal also will impact the performance. So we gain above parameters via a large number of experiments. According to experiments, we determine an optimum value for the parameters to use in this application. The experiments show that the new method not only has better recovery quality and higher PSNRs, but also can achieve optimization steadily and effectively.

Sign in / Sign up

Export Citation Format

Share Document