Retinex-Based Laplacian Pyramid Method for Image Defogging

Zhou, Jingchun; Zhang, Dehuan; Zou, Peiyu; Zhang, Weidong

doi:10.1109/access.2019.2934981

Cited by 45 publications

(22 citation statements)

References 50 publications

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“…The corresponding image processing examples are also presented. First, the Laplacian pyramid subsampling [16] is used to highlight the ISN. Because the width of ISN is larger than the width of the normal interference stripe; after the subsampling, the ISN still can be retained while the normal stripe will be protected to some extent.…”

Section: Computational Flow Chartmentioning

confidence: 99%

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

Liu

Song

et al. 2021

IEEE Photonics J.

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Comparing with the application of charge coupled device (CCD) sensor, a kind of interference stripe noise (ISN) can be observed easily from the complementary metal-oxide-semiconductor (CMOS) sensor-based digital holographic measurement (DHM) system because a series of optic coatings and filters are used in front of that sensor in order to improve its imaging effect. The improper assembly or the micro manufacturing error of these optic components may create the unexpected interference phenomena in the imaging sensor. To reduce that noise, an effective image processing method is developed. On one hand the Laplacian pyramid method is used to highlight the ISN and a cartoon-texture decomposition technique is carried out to remove the additive ISN. On the other hand, an improved Lee filtering is performed to restrain the multiplicative ISN. Extensive experiment results have shown that the image quality can be improved after the ISN reduction and this method is fit for solving a deionized water temperature estimation application where the texture complexity of target image is relatively low.

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Section: Computational Flow Chartmentioning

confidence: 99%

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

Liu

Song

et al. 2021

IEEE Photonics J.

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“…Many underwater image dehazing methods have been proposed by different scholars in the past decades. e existing underwater defogging technologies can be grouped into five categories, which contain enhancement method based on multiple images [5][6][7][8][9], image restoration method [10][11][12][13][14][15][16][17][18][19], image enhancement method [20][21][22][23][24][25], underwater image dehazing method based on deep learning [26][27][28], and the method based on fusion [29][30][31][32][33]. e existing underwater image dehazing methods have achieved some success, but there are still some shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

Zhou

Zhang

2020

Mathematical Problems in Engineering

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To solve the color cast and low contrast of underwater images caused by the effects of light absorption and scattering, we propose a novel underwater image enhancement method via bi-interval histogram equalization. The proposed method consists of three main parts: color correction, contrast enhancement, and multiscale fusion. First, the color cast is eliminated by automatic white balancing. Then, homomorphic filtering is adopted to decompose the image into high-frequency information and low-frequency information, the high-frequency information is enhanced by the gradient field bi-interval equalization which enhances the contrast and details of the image, and the low-frequency information is disposed via gamma correction for adjusting the exposure. Finally, we adopt a multiscale fusion strategy to fuse the high-frequency information, high-frequency after bi-interval equalization, and low-frequency information based on contrast, saturation, and exposure. Qualitative and quantitative performance evaluations demonstrate that the proposed method can effectively enhance the details and global contrast of the image and achieve better exposedness of the dark areas, which outperforms several state-of-the-art methods.

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“…Most existing dehazing algorithms are based on image enhancement [2], [3], physical models [4], [5], [6] or machine learning [7], [8]. These algorithms can be divided into two categories, namely those with and without noise suppression.…”

Section: Introductionmentioning

confidence: 99%

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

2021

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In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

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Retinex-Based Laplacian Pyramid Method for Image Defogging

Cited by 45 publications

References 50 publications

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

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