Design and Hardware Implementation of Image Compression Denoising Based on Median Filter and Wavelet Transform

2014 ◽  
Vol 602-605 ◽  
pp. 3218-3222
Author(s):  
Wei Qu ◽  
Xiao Xin Sun
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Image Compression ◽  
Hardware Implementation ◽  
Median Filter ◽  
Processing System ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Denoising Method

An image compression denoising method based on median filter and wavelet transform is proposed in order to overcoming shortcomings of traditional methods of image processing in this paper. This method combined hardware parallelism with software technology is enable to achieve image compression denoising and take into account algorithm validation, and fast response of the system. An real-time image processing system is design by this method. Design and hardware implementation of fast median filtering algorithm based on EP1C12 FPGA chip is realized and software simulation of median filter and wavelet transform is done. The experimental results show that this system has advantages of fast response characteristic, less time consuming and high signal to noise ratio.

Comparison between Mean Filter and Median Filter Algorithm in Image Denoising Field

2014 ◽  
Vol 644-650 ◽  
pp. 4112-4116 ◽  
Author(s):  
Xiao Xin Sun ◽  
Wei Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Signal To Noise ◽  
Denoising Method ◽  
Mean Filter ◽  
Noise Ratio

An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

Comparison and Improvements of Image Denoising Based on Wavelet Transform

2015 ◽  
Vol 740 ◽  
pp. 644-647
Author(s):  
Xue Mei Xiao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Important Application ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.

Real-Time Primary Image Processing

2013 ◽  
pp. 33-53
Author(s):  
Radu Dobrescu ◽  
Dan Popescu
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Median Filter ◽  
Processing System ◽  
Input Image ◽  
Sensing Element ◽  
Software Configuration ◽  
Software Algorithms ◽  
Primary Image ◽  
High Level

Image processing operations have been classified into three main levels, namely low (primary), intermediate, and high. In order to combine speed and flexibility, an optimum hardware/software configuration is required. For multitask primary processing, a pipeline configuration is proposed. This structure, which is an interface between the sensing element (camera) and the main processing system, achieves real time video signal preprocessing, during the image acquisition time. In order to form the working neighborhoods, the input image signal is delayed (two lines and three pixels). Thus, locally 3×3 type processing modules are created. A successive comparison median filter and a logical filter for edge detection are implemented for a pipeline configuration. On the other hand, for low level, intermediate, and high level operations, software algorithms on parallel platforms are proposed. Finally, a case study of lines detection using directional filter discusses the performance dependency on number of processors.

Research and Exploration on Static Image Compression Technology Based on JPEG2000

2014 ◽  
Vol 644-650 ◽  
pp. 4182-4186
Author(s):  
Hua Tian ◽  
Ming Jun Li ◽  
Huan Huan Liu
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Processing System ◽  
Coding System ◽  
Compression Process ◽  
Static Image ◽  
Compression Time ◽  
Speedup Ratio ◽  
Universal Applicability ◽  
Static Form

This article introduces GPU-accelerated image processing parallel computing technology into standard core coding system of JPEG2000 static image compression and accelerates and designs the image compression process using CUDA acceleration principle. It also establishes the algorithm of image pixel array layered and reconstruction coding and realizes the coding of this algorithm using VC software. In order to verify the effectiveness and universal applicability of the algorithm and procedures, this paper compresses four images of different sizes and pixels in the static form of the JPEG2000. Through the comparison of the compression time, we can find that GPU hardware image processing system has a higher speedup ratio. With the increase of pixel and size, speedup ratio gradually increased which means that GPU acceleration has good adaptability.

scholarly journals X-ray image denoising based on wavelet transform and median filter

2020 ◽  
Vol 5 (2) ◽  
pp. 435-442
Author(s):  
Hanlei Dong ◽  
Liguo Zhao ◽  
Yunxing Shu ◽  
Neal N. Xiong
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Median Filter ◽  
Coefficient Matrix ◽  
Wavelet Domain ◽  
Denoising Method ◽  
X Ray ◽  
Standard Pulse ◽  
Soft Threshold ◽  
The Difference

AbstractThis paper mainly proposed and researched based on wavelet transform, and then used the X-map denoising technique of value filter. In other words, the value image was filtered in the spatial domain, and the value filtering was used as the standard pulse (salt) noise, also used as in the wavelet domain. After the filtered image was decomposed by biorthogonal double wavelet transform, a wavelet coefficient matrix was generated, and a soft threshold quantisation process was performed on the wavelet coefficients to produce a new wavelet coefficient matrix. In the end, they used a new wavelet coefficient matrix for image reconstruction. The processing resulted that the denoising method proposed in this paper showed that the X image can be denoised, which not only reduced the X-picture-like noise but also preserved the X-picture-like details as much as possible. It also helped to enhance diagnostic accuracy and reduced the difference in reading.

Study and Design of Image Pre-Processing System Based on FPGA

2012 ◽  
Vol 482-484 ◽  
pp. 200-205
Author(s):  
Jin Tong Li ◽  
Xiao Jie Duan
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
High Speed ◽  
Median Filter ◽  
Processing System ◽  
Image Preprocessing ◽  
Sobel Operator ◽  
Real Time Image ◽  
Mean Filter ◽  
Time Image

The high speed of image processing is required in the real-time image processing system. To improve the image processing speed, it is mainly implemented by hardware. This thesis constructs an image preprocessing system based on FPGA. Several typical image preprocessing algorithms, such as fast median filter, mean filter, Gaussian filter, Laplacian and Sobel operator edge filters, are implemented in the system. In the process of the system design, the characteristics and requirements of the image-processing implemented by hardware are fully considered. The parallel attribute insiding the image algorithm is effectively dug and the pipeline structure is adopt. All mentioned above are useful to optimize and improve the algorithms, reduce the hardware consumption and raise speed of image processing.

Sign in / Sign up

Export Citation Format

Share Document