Research on High Resolution Image Processing System Based on FPGA Technology

Real-time high-accuracy localization has a wide range of applications in scenarios, such as pedestrian navigation, emergency rescue, and vehicle networks. In these conditions, the measurement models are often nonlinear, and traditional Kalman and particle filters cannot provide long-time high-precision location-based services. To this end, we propose a Gaussian condensation filter (GCF) algorithm that can achieve high-accuracy localization in a harsh environment. However, aiming at the degradation of sampling points in target tracking based on the GCF, this article proposes a GCF algorithm based on particle flow which transfers the sample points satisfying the prior distribution of the target state to the posterior distribution, thereby improving the practical accuracy of the target-tracking algorithm. Further, to enhance the information fusion in the cooperative network, we propose a multitarget cooperative tracking algorithm to accomplish spatially constrained timing filtering of state information for improving the error correction of the target nodes on timing estimation. Numerical simulations are conducted to determine the effectiveness of our proposed algorithms. Compared with the GCF, its positioning accuracy is improved to 44.6%. Compared with the Gaussian condensation algorithm based on particle flow (PF), the practical accuracy of the GCF algorithm based on cooperative constrained PF in multitarget tracking is improved to 58.1%.

The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

A Study on Development of Optimal Noise Filter Algorithm for Laser Vision System in GMA Welding

Image processing has an important role in signal processing. The occurrence of noise in the image can degrade its quality and can cause loss of information. In this paper, design of Gaussian, mean, and median filters is considered. Gaussian and mean filters are linear filters and are designed using convolution between 3 × 3 image pixel matrix and kernel matrix. The median filter is a nonlinear filter. An SRAM-based FPGA implementation of such a filter is susceptible to memory bit flips that are caused by single-event upsets (SEUs). Hence, a protection method is needed to ensure proper working of the median filter. Here, the median filter design proposed in (Aranda et al. in J. IEEE Transactions on Nuclear Science 64:2219–2226, 2017 [7]) is used to check if the median value obtained is within a dynamic range. The performance of these three filters is evaluated by considering image quality metrics such as PSNR and correlation coefficient with four different noises. The results show choice of the filter for denoising, based on the type of noise present in the image. In recent times, FPGA technology has become a reliable method for implementing image processing algorithms due to their fast response compared to image processing in MATLAB. The noisy image to pixel conversion is done in MATLAB. The 3 × 3 sliding window approach is used for filter coding in Verilog, and the FPGA synthesis on virtex5 is done with Xilinx ISE 14.5. The images are reconstructed with filtered pixels in MATLAB.

Stress field is an important parameter for determining the strength of a plate due the loading. Measurement of the stress field in it cannot be done directly. The mathematic explanation shows that the stress field has a relationship with the terrain displacement/displacement. In the plate theory, deflection is defined as displacement towards out plane direction. One method to get the value of deflection on the plate is a moire method. This moire method uses the principle of line superposition between plate and its reference. When a test plate subjected to a load, the deflection that occurs in the test material a pattern will be produced in the form of a superposition of light and dark pattern (fringe) which describes the distribution of the stress-displacement on a surface of tested material. Tested material is AISI 304.The surface of tested material was given a lattice pattern with variations in distance of 1 mm. The fourth side of the tested plate is clamped perfectly, then the center of it will be pressurized with variations in displacement (0.5, 1, 1.5, and 2 mm). The occurred Moire patterns will be captured by the camera to turn it into a digital image. Furthermore, it will be processed through a series of image processing which consists of the four different filter algorithms (Gaussian filter and Butterworth Low Pass Filter), and also four edge detection algorithms (Sobel, Prewitt, Canny, and Roberts).The results of the stress-strain measurements will be compared with the results of analytical calculations and numerical calculations. The difference of displacement average using ​​moire method compared with analytical method is 6.75%, while using numerical method is 7.55%. Furthermore the difference of stress average using ​​moire method compared with analytical calculation is 9:08%, while using numerical methods by 9.5%.

Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.

An analysis of the geometric distortions produced by median and related image processing filters

Edge detection is the first step for image segmentation in image processing. So far there are many algorithms for edge detection. In the paper, a novel algorithm about edge detection is based on the median filtering and Sobel operator. It has strong immunity of noise, edges exquisite and better recognition compared to ordinary sobel operator.

Image filtering involves taking a digital image and producing a new image from it. In software packages such as Adobe's Photoshop, image filters are used to produce artistic versions of original images. Such software usually includes hundreds of different image filtering algorithms, each with many fine-tuneable parameters. While this freedom of exploration may be liberating to artists and designers, it can be daunting for less experienced users. Photoshop provides image filter browsing technology, but does not yet enable the construction of a filter which produces a reasonable approximation of a given filtered image from a given original image. We investigate here whether it is possible to automatically evolve an image filter to approximate a target filter, given only an original image and a filtered version of the original. We describe a tree based representation for filters, the fitness functions and search techniques we employed, and we present the results of experimentation with various search setups. We demonstrate the feasibility of evolving image filters and suggest new ways to improve the process.

The use of proton therapy has the advantage of high dose concentration as it is possible to concentrate the dose on the tumor while suppressing damage to the surrounding normal organs. However, the range uncertainty significantly affects the actual dose distribution in the vicinity of the proton range, limiting the benefit of proton therapy for reducing the dose to normal organs. By measuring the annihilation gamma rays from the produced positron emitters, it is possible to obtain a proton induced positron emission tomography (pPET) image according to the irradiation region of the proton beam. Smoothing with a Gaussian filter is generally used to denoise PET images; however, this approach lowers the spatial resolution. Furthermore, other conventional smoothing processing methods may deteriorate the steep region of the pPET images. In this study, we proposed a denoising method based on a Residual U-Net for pPET images. We conducted the Monte Carlo simulation and irradiation experiment on a human phantom to obtain pPET data. The accuracy of the range estimation and the image similarity were evaluated for pPET images using the Residual U-Net, a Gaussian filter, a median filter, the block-matching and 3D-filtering (BM3D), and a total variation (TV) filter. Usage of the Residual U-Net yielded effective results corresponding to the range estimation; however, the results of peak-signal-to-noise ratio were identical to those for the Gaussian filter, median filter, BM3D, and TV filter. The proposed method can contribute to improving the accuracy of treatment verification and shortening the PET measurement time.

Median filtering is an important approach in digital image processing for noise elimination. An improved median filtering algorithm (IMFA) is proposed which can be implemented with only 17 comparisons and 6 clocks delay for 3×3 median filtering mathematical model based on field programmable gate array (FPGA). The algorithm benefits from the parallel processing and pipelining structure of FPGA hardware. At first, the characteristics, basic operational principle and computing process of the IMFA are presented. And then the algorithm using modular technique and top-down design flow methodology with Verilog HDL are programed. At last, some simulation verifications for the algorithm by ModelSim and experimental verification on FPGA hardware platform are carried out. The IMFA can get a large number of data throughput and more quickly processing speed and less hardware resources than similar filtering algorithms.

A novel algorithm for noise detection and filtering is proposed in this paper based on space-time characteristics of nautical radar data.The noise can be detected and filtrated by the improved median filtering algorithm according to the uncorrelated property among various scans.The algorithm for traditional median filtering and that for mean filtering are analyzed in comparison with the algorithm proposed in this paper,and the evaluation parameter of SNR is utilized to estimate the results.It is shown that the algorithm proposed in this paper preserves not only such advantages of the traditional median filtering as simple calculation and high speed computability but also the gray information of the original image.In addition,a good noise reduction effect can be achieved.

With the increasing requirement of processing power in image processing, it is important to provide a real time, low-cost, and high performance platform. This paper presents a general-purpose FPGA-based dynamic reconfigurable platform for image processing. It uses the FPGA's powerful parallel processing architecture to implement the acceleration of the image algorithm. Based on the proposed system level architecture, our system includes different functional modules, such as edge detection and image filtering, which provides the flexibility for the system. According to the different application requirements, this design strategy can dynamic reconfigure the functional modules on line. Its hardware efficiency and software flexibility have been shown by the functional verification of the edge detection and image filtering algorithm on the Virtex-4 development platform.

Kalman filter tracking in a Cherenkov neutrino telescope

Since the computer vision technologies are used to extract features from the edges of images, the median filter is frequently used as a pre-processing operation to improve the quality of feature objects. However, the median filter algorithms still have the limitation on integrating with the mobile systems due to the large computation overhead. Since the median filter accounts for a large proportion of total computation, this paper proposes a separable median filter to resolve the limitation. The proposed separable median filter is implemented with the sorting network, and reuses the data sorted at neighbor pixels, reducing duplicated comparisons.