Analysis of median filter

Devarajan, G.; Aatre, V. K.; Sridhar, C. S.

doi:10.1109/ace.1990.762694

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…In the next step, the median [12] of the pixels is employed as a dark channel. Since the information of any image will hide in the middle of the image so, instead of finding the minimum of pixels in [4] we proposed the median.…”

Section: Dark Channel Prior Methods (Dcm)mentioning

confidence: 99%

Fast and memory efficient de-hazing technique for real-time computer vision applications

2020

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Some features of an image may spoil due to fog or haze, smoke. These images lose their brightness due to air-light scattering. It offers troublesomeness to the people lives in hill and fog regions of the world. This paper proposed two key aspects. One is a modified dark-channel method based on the median for eliminating the refine the transmission map as well as halos and artifacts, another important aspect is a memory-efficient row-based arrangement of the pixels for real-time applications. The advantage of this method is air-light can be predicted directly from the modified dark channel and also accurate transmission map can be estimated. This method is compared with other existing four algorithms. Our proposed method analyzed in terms of Peak Signal to Noise Ratio (PSNR), Average Time cost (ATC), percentage of haze improvement (PHI), average contrast of output image (ACOI), Mean Squared Error (MSE) and Structural Similarity Index (SSIM). The quality of the output de-haze image of our algorithm over existing algorithms is more. It has taken less computation time, equal MSE, with higher SSIM and has more percentage of haze improved over existing methods.Keywords Real-time image/video de-hazing · Median filter · Blocked random access memory (BRAM) · Row based pixel arrangement · Dark channel prior · Guided filter * Prathap Soma, prathap.

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Section: Dark Channel Prior Methods (Dcm)mentioning

confidence: 99%

Fast and memory efficient de-hazing technique for real-time computer vision applications

2020

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“…It can be done with a median filter. The smoothing process is done by determining the pixel statistics that are in the neighborhood area [15]- [17] Any potentially changing pixels will be replaced with the median value of the pixel value set that exists in the window of this neighborhood after ordering. The median filter is used to create a softer image than the original.…”

Section: Introductionmentioning

confidence: 99%

Effect of matrix size in affecting noise reduction level of filtering

Khairul

Wijaya

Siahaan³

et al. 2018

IJET

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Improved image quality needs to be done to improve data processing on the image. This quality improvement can be made by doing masking technique. Median Filter is one technique to enhance the quality of an image in a particular space. Median filtering improves the image by specifying a specific pixel from its neighboring pixels. The median filtering calculation uses a matrix block with an odd number. Each matrix block will have a middle value after the pixel values have regularly been sorted. This method is included in the category of nonlinear filtering. With the median filtering, the output pixel value is determined by the median of the specified mask environment. Median Filter has different results when using different matrix sizes as well. The results of this process can determine how gentle the result of noise reduction. In general, the larger the size of the matrix, the higher the blurriness of an image.

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