Multi-stage image denoising with the wavelet transform

Tian, Chunwei; Zheng, Menghua; Zuo, Wangmeng; Zhang, Bob; Zhang, Yanning; Zhang, David

doi:10.1016/j.patcog.2022.109050

Cited by 167 publications

(57 citation statements)

References 38 publications

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“…The improved algorithm maintains the shift operation and strengthens parallel computing, which reduces the difficulty of hardware implementation. The original 5/3 integer lifting algorithm is as follows: 18 c(2n+1)=x(2n+1)−⌊x(2n)+x(2n+2)2⌋,d(2n)=x(2n)+⌊c(2n+1)+c(2n−1)+24⌋,where x(2n) is original signal, c(2n+1) is wavelet coefficient, and d(2n) is scale coefficient.…”

Section: Adaptive Wavelet Transform Defoggingmentioning

confidence: 99%

Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

Dong,

Wang,

Deng

et al. 2024

J. Electron. Imag.

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In the context of real-time video defogging, accurately estimating the transmittance of sky regions and deep fault regions remains a challenging task due to severe color distortion and halo effects. To address this issue, we propose an innovative adaptive wavelet transform defogging (AWTD) scheme aimed at precisely modifying the transmittance of the sky and deep fault regions. Additionally, we present a dedicated field programmable gate array defogging architecture capable of direct application for real-time synchronous defogging of Full HD (1920 × 1080) videos. Through extensive experiments and comparisons, we demonstrate the effectiveness and practicality of the proposed AWTD approach in mitigating color distortion, halo effects, and other fog-induced artifacts. The results show that the dehazing algorithm attains 60 frames per second for the image resolution of 1920 × 1080, which is suitable of real time applications.

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Section: Adaptive Wavelet Transform Defoggingmentioning

confidence: 99%

Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

Dong,

Wang,

Deng

et al. 2024

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…These days, it is common to apply convolutional neural networks (CNNs) [30] for aesthetic evaluation tasks. CNNs are well-known for their end-to-end Manuscript submitted to ACM prediction power, which has achieved impressive record-breaking results on several computer vision tasks [31,32,33,34]. Several existing studies [35,36,37] attempted to apply the CNN model to the categorisation of fashion products, achieving a remarkable accuracy.…”

Section: Related Workmentioning

confidence: 99%

GEO: A Computational Design Framework for Automotive Exterior Facelift

Huang

Chen

Yan

et al. 2023

ACM Trans. Knowl. Discov. Data

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Exterior facelift has become an effective method for automakers to boost the consumers’ interest in an existing car model before it is redesigned. To support the automotive facelift design process, this study develops a novel computational framework – Generator, Evaluator, Optimiser (GEO), which comprises 3 components: a StyleGAN2-based design generator that creates different facelift designs; a convolutional neural network (CNN)-based evaluator that assesses designs from the aesthetics perspective; and a recurrent neural network (RNN)-based decision optimiser that selects designs to maximise the predicted profit of the targeted car model over time. We validate the GEO framework in experiments with real-world datasets and describe some resulting managerial implications for automotive facelift. Our study makes both methodological and application contributions. First, the generator’s mapping network and projection methods are carefully tailored to facelift where only minor changes are performed without affecting the family signature of the automobile brands. Second, two evaluation metrics are proposed to assess the generated designs. Third, profit maximisation is taken into account in the design selection. From a high-level perspective, our study contributes to the recent use of machine learning and data mining in marketing and design studies. To the best of our knowledge, this is the first study that uses deep generative models for automotive regional design upgrading and that provides an end-to-end decision-support solution for automakers and designers.

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“…They used batch renormalization to overcome small mini-batch problems. In [22], to address the shortcomings of lightweight CNNs' limited performance in terms of network depth and width and to achieve a trade-off between denoising performance and computing costs, Tian et al used a dynamic convolutional block. They also combined wavelet transform which is a signalprocessing technique and residual dense block to recover more detailed information while the noise removal process.…”

Section: A Related Workmentioning

confidence: 99%

MLFAN: Multilevel Feature Attention Network With Texture Prior for Image Denoising

2023

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Machine learning techniques, especially deep learning, have made great achievements in computer vision including image denoising recently. However, in most convolutional neural network (CNN) based methods presented for image denoising, convolutional kernels are considered for only one scale and more scales are neglected mostly. Studies on multilevel feature extraction treat these features as if they have the same importance and do not use a mechanism such as feature attention for their weighting. Also, for effective noise removal, edge information is used as prior knowledge, but texture information is generally disregarded. This study has focused on these shortcomings and introduced a new attention-based CNN for image denoising. The main contributions of this study are as follows: First, we propose a CNN-based network to extract Local Binary Pattern (LBP) from the noisy image for texture information. So, we use texture information as prior knowledge for the preservation of details in the evolved image during the denoising process. Besides we propose a new multilevel feature extraction block to get different level features. After extracting multilevel features using feature attention, we weight these different levels of features. In addition to this, we introduce a multilevel feature attention network (MLFAN) for noise removal by combining them. The comprehensive experimental results show that our MLFAN noise reduction network can effectively remove Gaussian noise from images and compared with some stateof-the-art denoising methods, it outperforms in terms of both quantitative and qualitative evaluations. For Set12 grey image set, and McMaster color image set, MLFAN gives PSNR={33.

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Multi-stage image denoising with the wavelet transform

Cited by 167 publications

References 38 publications

Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

GEO: A Computational Design Framework for Automotive Exterior Facelift

MLFAN: Multilevel Feature Attention Network With Texture Prior for Image Denoising

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