Hardware Implementation of Histogram-Based Algorithm for Image Enhancement

Chinchwadkar, Renuka M.; Ingale, Vaishali; Gokhale, Ashlesha

doi:10.1007/978-981-15-4029-5_6

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The primary goal of an image enhancement method is to improve the brightness, contrast, and visual quality of an image for human viewing [16], [17]. There are lots of image enhancement applications, such as biomedical applications [18], and Haze Removal [4], [17].…”

Section: Related Workmentioning

confidence: 99%

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

Azhari

Setumin

Noorsal

2023

IJECE

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A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

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Section: Related Workmentioning

confidence: 99%

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

Azhari

Setumin

Noorsal

2023

IJECE

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“…The primary goal of image enhancement was to improve the brightness, contrast, and improved visual quality of an image for human viewing [11], [12]. Image enhancement evolves in a wide range of applications such as biomedical [8], and haze removal [6], [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The visual appearance of an image can be improved by using the histogram. The dynamic range of pixels, contrast, and many other issues that arise during the acquisition of an image is shown in the histogram [11].…”

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

confidence: 99%

“…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%

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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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