Implementation of a Novel, Fast and Efficient Image De-Hazing Algorithm on Embedded Hardware Platforms

Soma, P.; Jatoth, Ravi Kumar

doi:10.1007/s00034-020-01517-4

Cited by 9 publications

(10 citation statements)

References 26 publications

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“…The PSNR and the MSE represent overall error content in the entire output image. They are well-known performance metrics for assessing the degree of inaccuracy [4]. In order to assess the quality of the hazy/foggy images, measures like MSE and PSNR are frequently used [29].…”

Section: Performance Evaluation Methodsmentioning

confidence: 99%

“…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]. Dong et al [19] proposed a fast, efficient algorithm for the enhancement of low-light video.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed algorithm includes a detail enhancement algorithm in the pre-processing stage to restore the faded details of the input image. Weight maps were first calculated using dark channels to Another application of the image enhancement method that uses the de-hazing algorithm was proposed by Soma and Jatoth [4]. Two methods for hardware implementation of the image de-hazing algorithm were discussed in the paper.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to that, an image that suffers from contrast is usually caused by a haze. It limits the visibility of distant scenes, hence causing the image to look blurry [4]. Therefore, contrast enhancement of an image is important to remove the haze and to increase the visibility of an image.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Performance Evaluation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Azhari

Setumin

Noorsal

2023

IJECE

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“…En cuanto a pruebas de desempeño (benchmarks), en [9] se evalúan diferentes algoritmos, mencionando al DCP como uno de los métodos que mejores resultados lograron en un ambiente submarino. En [10] se realizó un benchmark para dos plataformas embebidas diferentes, el cual tiene similitudes con los objetivos de este estudio. Sin embargo, este benchmark se enfocan en arquitecturas especializadas mientras que la intención detrás de este trabajo es tratar con una amplia gama de plataformas, analizando más dispositivos y produciendo una prueba que se puede ejecutar sin cambios significativos en el código fuente, al proveer una funcionalidad para incluir otras arquitecturas no contempladas en este trabajo.…”

Section: Trabajos Relacionadosunclassified

Perfilado de rendimiento de FPS para múltiples arquitecturas computacionales usando el algoritmo de reducción de neblina DCP

Navarro-Brenes

Chavarría-Zamora

2022

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Este documento presenta una prueba de rendimiento creada para evaluar el desempeño de diferentes plataformas en la ejecución de un algoritmo de reducción de niebla basado en el “Dark Channel Prior” (DCP) [1]. El parámetro utilizado para la evaluación fue el número de cuadros por segundo (FPS, por sus siglas en inglés) que el dispositivo es capaz de procesar. Con esta herramienta se logra determinar aquellas arquitecturas que son aptas para ejecutar el algoritmo en tiempo real. El ambiente de pruebas se ejecutó en cuatro plataformas, un Google Pixel 3a, una Raspberry Pi 3B+, una GPU de NVIDIA y un procesador Intel x86. Se usaron los siguientes kits de desarrollo de software (SDK, por sus siglas en inglés) según la plataforma: Android NDK, Yocto Poky, CUDA y la cadena de herramientas GCC. La herramienta permitió recopilar, para cada plataforma, los FPS para distintos tamaños de imagen, con estos resultados se pueden escoger la arquitectura más idónea según el área de implementación (e.g., bajo consumo o HPC). El ambiente de pruebas se ejecutó en cuatro plataformas, un Google Pixel 3a, una Raspberry Pi 3B+, una GPU de NVIDIA y un procesador Intel x86. Se usaron los siguientes kits de desarrollo de software (SDK, por sus siglas en inglés) según la plataforma: Android NDK, Yocto Poky, CUDA y la cadena de herramientas GCC. La herramienta permitió recopilar, para cada plataforma, los FPS para distintos tamaños de imagen, con estos resultados se pueden escoger la arquitectura más idónea según el área de implementación (e.g., bajo consumo o HPC).

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A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods

Agrawal

Jalal

2022

Arch Computat Methods Eng

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Implementation of a Novel, Fast and Efficient Image De-Hazing Algorithm on Embedded Hardware Platforms

Cited by 9 publications

References 26 publications

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

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

Perfilado de rendimiento de FPS para múltiples arquitecturas computacionales usando el algoritmo de reducción de neblina DCP

A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods

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