Implementation of a fixed-point 2D Gaussian Filter for Image Processing based on FPGA

Cabello, Frank C.; León, Julio; Iano, Yuzo; Arthur, Rangel

doi:10.1109/spa.2015.7365108

Cited by 49 publications

(22 citation statements)

References 7 publications

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“…As we can see, kernel size have a big influence in design performances so that area occupation increase by the increase of kernel size. Our results outperform those in [12] in term of slices registers and LUTs by 6% and 15% for fixed arithmetic using kernel size [3 × 3]. For floating arithmetic and [3 × 3] kernel size, the area use decrease by 8% and 39% in term of slices registers and LUTs.…”

Section: (Ijacsa) International Journal Of Advanced Computer Science Andmentioning

confidence: 67%

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FPGA implementation of filtered image using 2D Gaussian filter

Kabbai¹,

Sghaier²,

Douik³

et al. 2016

ijacsa

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Abstract-Image filtering is one of the very useful techniques in image processing and computer vision. It is used to eliminate useless details and noise from an image. In this paper, a hardware implementation of image filtered using 2D Gaussian Filter will be present. The Gaussian filter architecture will be described using a different way to implement convolution module. Thus, multiplication is in the heart of convolution module, for this reason, three different ways to implement multiplication operations will be presented. The first way is done using the standard method. The second way uses Field Programmable Gate Array (FPGA) features Digital Signal Processor (DSP) to ensure and make fast the scalability of the effective FPGA resource and then to speed up calculation. The third way uses real multiplier for more precision and a the maximum uses of FPGA resources. In this paper, we compare the image quality of hardware (VHDL) and software (MATLAB) implementation using the Peak Signal-to-Noise Ratio (PSNR). Also, the FPGA resource usage for different sizes of Gaussian kernel will be presented in order to provide a comparison between fixed-point and floating point implementations.

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Section: (Ijacsa) International Journal Of Advanced Computer Science Andmentioning

confidence: 67%

“…Table III compares the results of both fixed point arithmetic and floating point arithmetic. As we can see, we decrease the number of slice registers and slice LUTs comparing to [12].…”

Section: (Ijacsa) International Journal Of Advanced Computer Science Andmentioning

confidence: 88%

FPGA implementation of filtered image using 2D Gaussian filter

Kabbai¹,

Sghaier²,

Douik³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…This represents a 371% increase in speed compared to a conventional FP32 multiplier and a 187% increase compared to a MB multiplier. A Floating-Point (FP) implementation on 16 bits of the proposed design has been also developed at the end of comparing the proposed architecture with the one presented in [21]. This design has been implemented on a Xilinx Spartan 6 board, using Nexys 3, in order to obtain a fair comparison with the design in [21].…”

Section: Resultsmentioning

confidence: 99%

Design of a Convolutional Two-Dimensional Filter in FPGA for Image Processing Applications

2017

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Exploiting the Bachet weight decomposition theorem, a new two-dimensional filter is designed. The filter can be adapted to different multimedia applications, but in this work it is specifically targeted to image processing applications. The method allows emulating standard 32 bit floating point multipliers using a chain of fixed point adders and a logic unit to manage the exponent, in order to obtain IEEE-754 compliant results. The proposed design allows more compact implementation of a floating point filtering architecture when a fixed set of coefficients and a fixed range of input values are used. The elaboration of the data proceeds in raster-scan order and is capable of directly processing the data coming from the acquisition source thanks to a careful organization of the memories, avoiding the implementation of frame buffers or any aligning circuitry. The proposed architecture shows state-of-the-art performances in terms of critical path delay, obtaining a critical path delay of 4.7 ns when implemented on a Xilinx Virtex 7 FPGA board

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“…The Gaussian filter is a low-pass filter commonly used in image processing applications to remove detail and noise in the image. Using the Gaussian filter, smoothing and blurring are applied on the image to remove the noise and improve the image quality [21][22]. With the Gaussian filtering method, various image/video processing applications such as edge detection, image blurring and mosaicization are easily performed [21].…”

Section: Gaussian (Smoothing / Blurring) Filtermentioning

confidence: 99%

A New Hybrid Filter Approach for Image Processing

Aksoy

Salman

2020

Sakarya University Journal of Computer and Information Sciences

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Today, with the rapidly advancing technology, the importance of image processing techniques is increasing. Image processing is used in many areas from facial recognition to plant disease identification. One of the important image processing stages is the filtering stage used for smoothing images and object detection. Among these filtering techniques, basic filtering techniques such as mean, median and Gaussian are used in image processing. However, these filtering techniques are known to be insufficient to achieve the desired results in some cases. In this study, a new hybrid filtering approach named Mean-Median-Gaussian (MMG) is presented using these three basic filtering techniques. It has been demonstrated that the obtained MMG hybrid algorithm gives more successful results than these three basic filtering techniques in smoothing the images and determining the boundary lines.

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Implementation of a fixed-point 2D Gaussian Filter for Image Processing based on FPGA

Cited by 49 publications

References 7 publications

FPGA implementation of filtered image using 2D Gaussian filter

FPGA implementation of filtered image using 2D Gaussian filter

Design of a Convolutional Two-Dimensional Filter in FPGA for Image Processing Applications

A New Hybrid Filter Approach for Image Processing

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