Single Image Haze Removal Using Hazy Particle Maps

Kim, Geun-Jun; Lee, Seungmin; Kang, Bongsoon

doi:10.1587/transfun.e101.a.1999

Cited by 12 publications

(46 citation statements)

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“…The median filter tends to blur the image edges with depth discontinuities. To solve this problem, Kim et al [24] proposed using a modified hybrid median filter in place of the standard median filter to provide better performance at image edges when estimating the airlight. This method will be discussed in Section 2.4.…”

Section: Fast Haze Removal With Median Filtersmentioning

confidence: 99%

“…Using median filters in Tarel et al's dehazing algorithm [16] causes halo artifacts because the standard median filtering is not appropriate for image regions containing edges. This problem can be resolved efficiently using mHMF rather than the standard median filter (SMF), as proposed by Kim et al [24]. mHMF first computes the medians of three windows: cross, diagonal, and square.…”

Section: Hazy Particle Mapmentioning

confidence: 99%

“…A myriad of algorithms have been proposed to recover the visual quality of weather-degraded images to be as similar as possible to the original ones taken under clear weather conditions. Low-light image enhancement [1][2][3][4], rain removal [5][6][7][8], and image dehazing [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are cases in point. Haze removal ones, of all the algorithms developed for visibility restoration, have positive impacts on both photography and computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, haze removal algorithms are categorized according to the number of input images they need. Due to sufficient input information, multi-image approaches [9][10][11][12] are superior to single-image ones [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] in terms of performance. Given the difficulty of collecting the external information, however, there is little interest from researchers.…”

Section: Introductionmentioning

confidence: 99%

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Improved Color Attenuation Prior for Single-Image Haze Removal

2019

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This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.

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Section: Fast Haze Removal With Median Filtersmentioning

confidence: 99%

Section: Hazy Particle Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Color Attenuation Prior for Single-Image Haze Removal

2019

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“…The haze removal algorithms can be roughly divided into two main categories: multiple image processing [4,5] and single image processing [6][7][8][9][10][11][12][13][14][15][16][17]. Although multiple image processing is generally superior in performance, it is not attractive due to the high complexity of frame storage memories.…”

Section: Introductionmentioning

confidence: 99%

A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

2019

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This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.

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