Low light image enhancement with dual-tree complex wavelet transform

Jung, Cheolkon; Yang, Qi; Sun, Tingting; Fu, Qingtao; Song, Hyoseob

doi:10.1016/j.jvcir.2016.11.001

Cited by 49 publications

(29 citation statements)

References 25 publications

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“…C. Jung, Q. Yang, T. Sun, Q. Fu, and H. Song, [15] presented an efficient ICE approach namely Dual-Tree Complex Wavelet Transform (DT-CWT), which was used in many ranges of images without noise extension. For image enhancement, Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used in this research study.…”

Section: Related Workmentioning

confidence: 99%

Weber’s Law based Camera Response Model for Low Light Image Contrast Enhancement

Rao¹,

Srikrishna²,

Vadaparthi³

2019

IJIES

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In the field of digital image processing, Image Contrast Enhancement (ICE) is an emerging research area, which helps to attain the sub-standard quality images. The existing ICE techniques have a few limitations like brightness preservation and contrast limitation. Also, the existing ICE techniques affects from over-enhancement that leads to unrealistic results and halo-like artifacts in the high contrast region. To address these issues, a new contrast enhancement method (Second Order Derivative with Weber's Law (SODWL)) is developed in this research study. The proposed SODWL method includes two major phases (i) estimate the relationship between different image exposures and identify the appropriate Camera Response Model (CRM), (ii) illumination estimation method is applied to calculate the exposure ratio map. The SODWL method improved the performance of ICE effectively, because the developed approach considers even the small edge pixel values for contrast enhancement. After that, Weber's law is applied for adjusting the lighting conditions of the input image. The experimental analysis was conducted on the reputed datasets: VV, LIME-data, DICM, and MEF. Finally, the performance of proposed method is evaluated by applying the evaluation metrics such as, Lightness Order Error (LOE), and Visual Information Fidelity (VIF). The experimental result showed that the proposed system improved the VIF value upto 0.4-0.1 and also reduced the LOE value up to 2447-2 as related to the existing systems.

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Section: Related Workmentioning

confidence: 99%

Weber’s Law based Camera Response Model for Low Light Image Contrast Enhancement

Rao¹,

Srikrishna²,

Vadaparthi³

2019

IJIES

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“…The threshold α in the proposed method is set as 0.4, which came from experience prior. Figures 9,10,11,12,13,14,and 15 show part comparison of the proposed method with bright channel prior-based method [21], negative imaging dark channelbased method [20], multi-scale retinex (MSR) with customized sigmoid function [11], multi-scale retinex with color restoration (MSRCR) [13], and LIME [23].…”

Section: Experiments Settingmentioning

confidence: 99%

“…Compared to using multiple images for image enhancement, image enhancement with a single image does not need additional assistant images and has received increasing interests recently. For example, aiming at contrast enhancement, many conventional image processing techniques or their modifications, such as the well-known histogram equalization-based methods [6][7][8][9] and wavelet transform-based method [10], are widely employed to enhance the darks at the cost of saturating bright. In order to avoid structure information loss, retinex-based methods, including single-scale retinex (SSR), multiple scale-retinex (MSR)-based methods [11,12], and the MSR with color restoration (MSRCR) [13], were employed to suppress noise and preserve highlighted regions of the original image.…”

Section: Introductionmentioning

confidence: 99%

Nighttime low illumination image enhancement with single image using bright/dark channel prior

Shi

Zhu

Guo

et al. 2018

J Image Video Proc.

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Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

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“…In [30], DTCWT is used to create an effective contrast enhancement method and achieves a good performance in low illumination image enhancement. A new method for the interpolation of a full high-definition (HD) image which applied DTCWT to decompose the low-resolution image into different sub-bands was presented in [31].…”

Section: Dual-tree Complex Wavelet Transformmentioning

confidence: 99%

A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

Liu

Wang

et al. 2017

Applied Sciences

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Abstract:To eliminate the noise of infrared thermal image without reference and noise model, an improved dual-tree complex wavelet transform (DTCWT), optimized by an improved fruit-fly optimization algorithm (IFOA) and bilateral filter (BF), is proposed in this paper. Firstly, the noisy image is transformed by DTCWT, and the noise variance threshold is optimized by the IFOA, which is enhanced through a fly step range with inertia weight. Then, the denoised image will be re-processed using bilateral filter to improve the denoising performance and enhance the edge information. In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, and the denoising results are compared with other representative methods, such as DTCWT, block-matching and 3D filtering (BM3D), median filter, wiener filter, wavelet decomposition filter (WDF) and bilateral filter. Moreover, the proposed method is applied as pre-processing utilization for infrared thermal images in a coal mining working face.

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Low light image enhancement with dual-tree complex wavelet transform

Cited by 49 publications

References 25 publications

Weber’s Law based Camera Response Model for Low Light Image Contrast Enhancement

Weber’s Law based Camera Response Model for Low Light Image Contrast Enhancement

Nighttime low illumination image enhancement with single image using bright/dark channel prior

A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

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