Efficient Hardware Implementation of Switching Median Filter for extraction of Extremely High Impulse Noise Corrupted Images

Sadangi, Sushant; Baraha, Satyakam; Biswal, Pradyut Kumar

doi:10.1109/tencon.2019.8929543

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Using the Sobel operator, the paper [21] was able to identify diseases in hevea tree leaves by computing the gradient of each pixel in the image. Harris and susan [22] Histogram (brightness, contrast enhancement, region of interest) [11] Sobel Operator [21], [26] De-hazing [6], [12] Robert, Prewitt, Sobel, and Laplacian of Gaussian (LoG) operator masks [24] Power-of-two terms [24] Canny edge detection [23] Gaussian-based halo-reducing filter [7], [27] Biomedical image enhancement (BIE) [8] Modified context (MCT) [20] Median filter [28] Adding image [1] Adaptive histogram equalization (AHE) [19] 2D adaptive DIP [29] Boundary discriminative noise detection (BDND) [30] Guided image filtering and Halide [31] Discrete wavelet transform (DWT) [2] Contrast, brightness enhancement, image inverting, and threshold operation [32] Gaussian-based smoothing filter [33] Particle swarm [34] Medical image algorithm [35] Out of 13 papers that implemented image enhancement in hardware, only papers [12] and [6] can be compared as only these papers employs similar algorithm. Soma and Jatoth [6] proposed hardware implementation via the use of the Xilinx Zynq-706 FPGA board, while the paper [12] proposes the Xilinx Zynq-7000 FPGA board for its hardware implementation.…”

Section: Rq 1: Application Perspective: What Are the Most Common Appl...mentioning

confidence: 99%

A systematic literature review on hardware implementation of image processing

Azhari

Setumin²,

Rosli³

et al. 2023

IJRES

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Image processing has become under the spotlight recently and leads to a significant shift in various fields such as biomedical, satellite images, and graphical applications. Nevertheless, the poor quality of an image is one of the noticeable limitations of image processing as it restricts efficient data extraction to be conducted. Conventionally, the image was processed via software applications such as MATLAB. In spite of the software's ability to cater to the data extraction of low-quality image issues, it still suffers from the time-consuming issue. As the ability to obtain a rapid outcome is a favorable feature of efficient image processing, the use of hardware in image processing is deemed to keep the addressed issue at bay. Thus, the image enhancement techniques using hardware have gradually rising interest among researchers with numerous approaches such as field programmable gate array (FPGA). In this study, 25 different research papers published from 2016 to 2021 are studied and analyzed to focus on the performance of FPGA as hardware implementation in image processing techniques.

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Section: Rq 1: Application Perspective: What Are the Most Common Appl...mentioning

confidence: 99%

A systematic literature review on hardware implementation of image processing

Azhari

Setumin²,

Rosli³

et al. 2023

IJRES

View full text Add to dashboard Cite

show abstract

“…40%) while yielding higher image quality with respect to both PSNR and SSIM metrics. The switching median filter structure proposed in (Sadangi et al, 2019) in comparison uses more than four times resources. Similarly, the adaptive median filter by (Vasicek & Sekanina, 2007) uses more slice logic resources and can only handle SnP noise.…”

Section: Hardware Implementationmentioning

confidence: 99%

“…Parham et al (Taghinia Jelodari, Parsa Kordasiabi, Sheikhaei, & Forouzandeh, 2019) have described a hardware architecture for an adaptive median filter to suppress SnP noise using a switching mechanism based on noise density which is estimated using a local histogram of noisy pixel values. Recently, Sadangi et al (Sadangi, Baraha, & Biswal, 2019) have also described an FPGA implementation of adaptive median filter based on noise detection. Both of these architectures are inspired by the inability of a single median filtering kernel to effectively process images with different noise density.…”

Section: Introductionmentioning

confidence: 99%

SVM-Based Switching Filter Hardware Design for Mixed Noise Reduction in Digital Images Using High-Level Synthesis Tools

Dawrayn

Bilal

2021

International Journal of Computer Vision and Image Processing

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Impulse and Gaussian are the two most common types of noise that affect digital images due to imperfections in the imaging process, compression, storage and communication. The conventional filtering approaches, however, reduce the image quality in terms of sharpness and resolution while suppressing the effects of noise. In this work, a machine learning-based filtering structure has been proposed preserves the image quality while effectively removing the noise. Specifically, a support vector machine classifier is employed to detect the type of noise affecting each pixel to select an appropriate filter. The choice of filters includes Median and Bilateral filters of different kernel sizes. The classifier is trained using example images with known noise parameters. The proposed filtering structure has been shown to perform better than the conventional approaches in terms of image quality metrics. Moreover, the design has been implemented as a hardware accelerator on an FPGA device using high-level synthesis tools.

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