Fast Median Filter Research Articles

A new detail enhancement method for high dynamic range infrared image

Due to the width limitation of 8-bit data used in traditional display device, the local details and contrast of the infrared image are compressed. To cope with these limitations, a novel infrared image enhancement method based on CLAHE and multi-scale approach of DoG is proposed. First, the guided image filter is utilized to separate the base layer and detail layer of the image. Then, the improved CLAHE is used to adjust the base layer global contrast and the multi-scale detail boosting is used to enhance the detail features; for the detail layer, multi-scale fast median filter is used for noise reduction. Finally, the two layers are fused according to the weight of the detail layers’ noise level. Comparing with the real-time enhancement methods both in qualitative and quantitative aspects, our proposed method shows the better performance in experiment results.

Modified Pixels Based Fast Median Filter in Impulse Noise Environments

<p>This paper proposes a modified pixel-based fast median filter (MP-FMF) for impulse noise environments. The key idea behind MP-FMF is the reduction in the processing time by using modified pixels. It consists of three steps, namely, error detection, threshold decision, and noise mitigation. The presence of noise is detected by using modified pixels that include a cyclic redundancy check (CRC) function. Subsequently, the threshold values are decided by estimating the noise density. For noise mitigation, corrupted pixels are corrected with the neighboring pixels based on the principle of the median filter. The MP-FMF has a fast processing time and provides image quality correction and introduces features when the noise density is high. In addition, we introduce a new evaluation metric and investigate the performance of the proposed algorithm in terms of the quality, features, and computation time.</p>

Robust Lane-Mark Extraction for Autonomous Driving Under Complex Real Conditions

Lane marks on roads are among the most important items of road scene information in the process of autonomous driving, and lane-mark extraction based on visual cognitive computing is one of the most important components of advanced driving assistance systems in intelligent transportation system. Onboard cameras mounted on the front of autonomous vehicles capture road scene images from which lane marks are extracted. This paper proposes a new lane-mark extraction algorithm with four major parts. First, this paper handles the road images captured from onboard cameras by grayscale and fast median filter. Then, we exploit the characteristics of lane marks in road images as constraints to propose lane-features filter based on multi-constraints used to extract lane marks. Then, a clustering algorithm based on the double point removal of a ${p}$ -least squares algorithm is proposed to cluster features, and recursive dichotomy algorithm is used to fit the candidate lane marks. Finally, we carry out verification and optimization on candidate lane marks to obtain more accurate and stable extraction results. In our experiment, we divide the common complex road scenes into four categories. The results show that the proposed method can robustly extract lane marks under various complex real conditions. This paper also proposes forward a method to evaluate the results of lane-mark extraction, and partial test results are evaluated.

Enhanced Method of Removing Salt and Pepper Noise in Images Using an Adaptive Dual Threshold Fast Median Filter

This paper presents an enhanced adaptive dual threshold to remove salt and pepper noise in images using fast median filter. In the first stage, the adaptive dual threshold, a recent method used in signal processing, is employed to detect efficiently the pixels that are likely to be corrupted more by the salt and pepper noise. In the proposed algorithm, this technique is adaptive in the sense that the thresholding coefficient is automatically computed. In the second stage, the fast median filter will be applied only to the corrupted pixel. This pixel is changed either by the median value or by its neighbouring pixel value in the selected window. The performance evaluation of this algorithm is assessed using the peak signal-to-noise ratio (PSNR), the image enhancement factor (IEF) and correlation factor (CF). The simulation results have shown that the proposed method is significantly superior over some recently published works in terms of salt and pepper noise removal, especially when the images are corrupted by a high density noise.

Research of Automobile Collision Avoidance System Based on Video Image Processing

In this paper, an automobile collision avoidance system based on video image processing is proposed for vehicle active safety. Firstly, considering the requirement of real-time system, FPGA is responsible for the core task of video image processing. In the process, this paper introduces an image segmentation algorithm to extract the Region of Interest, a fast median filtering algorithm and an improved adaptive threshold Ostu segmentation algorithm. Secondly, in the design of alarm system, we use the STM32 chip which is based on Cortex-M3 kernel as the embedded platform. Finally, in order to analyze the data obtained by image processing and make smart decisions on the embedded platform, we establish a double parameter alarm mechanism of automobile security model. It can help the drivers to take appropriate hedging measures. The system in this paper has the characteristics of low cost, real-time and high reliability. Through the debugging and experiments, the system has reached the expected effect and met the requirements of automobile collision avoidance system.

Study on an Improved Median Filtering Algorithm

Median filter is a very effective method of non-linear smoothing filtering. However, the data ordering for traditional median filtering (TMF) is very time-consuming and hardly satisfy real-time image processing. This article proposes a kind of fast median filtering algorithm based on grey histogram, the filter seeks the median through the grey histogram of mask window, not the numeric sort, which decreases the comparison times. Moreover, the update of histogram by using the overlap of mask window increases the arithmetic processing speed. Meanwhile, in the proposed algorithm, the two-level self-adapting threshold comparison, with a higher precision of detection, is used to implement the inspection of noise point and improve the image quality and increase the signal-noise ratio by processing the noise point and non-noise point respectively. The experiments by matlab simulation can prove the availability of this algorithm.

Application of Improved Median Filtering Algorithm to Image De-Noising

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

Implementation of the Fast Median Filtering Algorithm Based on FPGA

Real-time image processing has been a difficult problem in embedded image processing system. The traditional MCU could not meet the real-time demand when large volume of data awaited to be proceed. FPGA is an effective driver to achieve real-time parallel processing of data. The implementation rationale and the design of module have been given in this article; and the Hard Software has been truly achieved. At the end of the article, the simulation waveform graph has been obtained by processing functional simulation on algorithm module by using Modelsim software; and the simulation result shows that this design is able to proper functioning and has good application prospects.

A combined post-filtering method to improve accuracy of variational optical flow estimation

We present a novel combined post-filtering (CPF) method to improve the accuracy of optical flow estimation. Its attractive advantages are that outliers reduction is attained while discontinuities are well preserved, and occlusions are partially handled. Major contributions are the following: First, the structure tensor (ST) based edge detection is introduced to extract flow edges. Moreover, we improve the detection performance by extending the traditional 2D spatial edge detector into spatial-scale 3D space, and also using a gradient bilateral filter (GBF) to replace the linear Gaussian filter to construct a multi-scale nonlinear ST. GBF is useful to preserve discontinuity but it is computationally expensive. A hybrid GBF and Gaussian filter (HGBGF) approach is proposed by means of a spatial-scale gradient signal-to-noise ratio (SNR) measure to solve the low efficiency issue. Additionally, a piecewise occlusion detection method is used to extract occlusions. Second, we apply a CPF method, which uses a weighted median filter (WMF), a bilateral filter (BF) and a fast median filter (MF), to post-smooth the detected edges and occlusions, and the other flat regions of the flow field, respectively. Benchmark tests on both synthetic and real sequences demonstrate the effectiveness of our method.

Detail Preserving Fast Median Based Filter

In this paper, a new nonlinear median based filtering technique is introduced for the enhancement of medical images that are highly contaminated by impulse noise. The proposed Detail Preserving Fast Median Filtering (DPFMF) technique is more effective in eliminating fixed value impulse noise and preserving the image details. The filter replaces a corrupted pixel by the median value of the fast median filter or by the value computed based on a unique criterion applied to the processed neighboring pixel values. The uncorrupted pixels are left undisturbed. Simulation studies show that the proposed filter can eliminate impulse noise of densities up to 80% while preserving the edges and fine details satisfactorily. The performance is evaluated by applying the proposed filter on different gray scale images and the superior results obtained in terms of subjective and objective evaluation are presented.

Pylon grid: A fast method for human head detection in range images

We present a fast and accurate method for human head detection in range images captured by a stereo camera that is positioned vertically, pointing from the roof to the ground. We show how a static grid of measure points (pylons) can outperform hill climbing techniques and how a fast median filter can be used for effective preprocessing of the range data. The Pylon Grid algorithm detects all local minima in the range image and has a linear time complexity with respect to the number of pylons. One important prerequisite for applying the Pylon Grid algorithm to human head detection is a one-to-one relationship between human heads in the scene and local minima in the range image. This is achieved in a preprocessing phase, where an orthographic projection, convexization and noise filtering is applied to the range data. The preprocessing steps also run in linear time and can be parametrized for a further trade-off between computational cost and accuracy. The method was tested with crowded scenes, where multiple dense groups of up to six people move in random directions and have physical contact.

Filter Window Characteristics Based Fast Adaptive Median Filter

This paper describes a Filter Window Characteristics based fast adaptive median filter algorithm. Experimental results from the point of view, this adaptive median filter algorithm to determine the degree of pollution on the image to adjust adaptive filtering process, while maintaining the average speed median filter algorithm based on filtering noise effectively, and better protection of the image the edge of the details.

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

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.

The Design of Image Acquisition and Preprocess System Based on FPGA

To deal with the large amount of data and complex computing problems during high-speed image acquisition, the image acquisition and preprocess system based on FPGA is designed in the paper. In order to obtain continuous and integrity of image data streams, the design has completed the acquisition of CCD camera video signal and implementation of de-interlacing ping-pong cache. The fast median filtering algorithm is used for image preprocessing, and finally the preprocessed image data is displayed on CRT. Experiments indicate that the design meets requirements of image sample quality and balances the real-time demand.

An Implementation and Improvement of Fast Two-Dimensional Median Filtering

This paper discussed a conventional fast median filtering algorithm for FPGA implementation. An improved way -- Quasi-median filtering algorithm -- have been proposed to reduce the occupancy rate of FPGA resources on the premise of ensuring the result of median filtering. Through the detailed analysis and comparison of results of simulation and experiments, conclusions can be drawn that such improvements can achieve better filtering results, and can reduce FPGA resource utilization. It offers some value for the application of design which requires more FPGA resources.

Study on low voltage electrical apparatus switching arc's image gathering and processing

Aiming at the shortcomings of the former arc high-speed image gathering system like high photography cost, long processing period and little available information, we comprehensively utilised the computer vision technology, the optoelectronic technology, the CCD high-speed image formation technology and the computer imagery processing technology to develop a new type of arc high-speed image gathering system including image gathering, datum transmission, datum memory, datum processing and so on. Using this system, we can successfully get a series of switching arc images from the burning arc to extinguished arc. In order to effectively suppress the noise and enhance the signal-to-noise ratio, this paper first applied the fast parallel median filter to eliminate stochastic noise, then used Laplacian sharpening to enhance the edge and the detail of image and made the final arc movement images clear enough to satisfy the request of arc image analysis and understanding. Through the analysis and understanding of a series of proceeded switching arc images from the burning arc to extinguished arc, we concluded the burning process characteristics of the low pressure contact device breaking arc and also we may discover that the obtained dynamic arc movement image characteristic has matched with massive vacuum arc simulation results.

Simple fast median filtering algorithm, with application to corner detection

The Letter studies the speed of operaton of median filtering algorithms. An iterative approach is investigated, and then simplified to take the local neighbourhood centre pixel as a starting approximation. Overall speedup factors of 2–4 are obtained. The method is useful when the ‘running median’ technique cannot be used, as for ‘skimmed’ median-based corner detectors.

Detail-preserving adaptive conditional median filters

We propose a conditional median filter that is able to preserve significantly more image detail than the conventional median filter when suppression of impulsive noise is desired. It does so by means of bypassing filtering when the local signal variation is below an adaptively adjusted threshold. Another advantage of our algorithm is that it is simple to implement and it can be used with any of the available fast median filter algorithms. In addition, it can be easily integrated into existing median filter hardware. We compare the complexity of our algorithm with that of another proposed algorithm with similar filtering objectives. The performance of the new algorithm on real images is compared to that of median and center-weighted median filters.