Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

Haouassi, Samia; Wu, Di

doi:10.3390/app10031190

Cited by 22 publications

(20 citation statements)

References 43 publications

(110 reference statements)

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“…The core idea is to minimize the mean square error between the original image and the restored image. According to this principle, the Wiener filtering equation can be deduced as follows [30]:…”

Section: Image Motion Deblurring Results and Analysismentioning

confidence: 99%

Metrology and Measurement Systems

Li,

Yu,

Liu

et al. 2021

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Effective recognition of tags in the dynamic measurement system would significantly improve the reading performance of the tag group, but the blurred outline and appearance of tag images captured in motion seriously limit the effectiveness of the existing tag group recognition. Thus, this paper proposes passive tag group recognition in the dynamic environment based on motion blur estimation and improved YOLOv2. Firstly, blur angles are estimated with a Gabor filter, and blur lengths are estimated through nonlinear modelling of a Generalized Regression Neural Network (GRNN). Secondly, tag recognition based on YOLOv2 improved by a Gaussian algorithm is proposed. The features of the tag group are analyzed by the Gaussian algorithm, the region of interest of the dynamic tag is effectively framed, and the tag foreground is extracted; Secondly, the data set of tag groups are trained by the end-to-end YOLOv2 algorithm for secondary screening and recognition, and finally the specific locations of tags are framed to meet the effective identification of tag groups in different scenes. A considerable number of experiments illustrate that the fusion algorithm can significantly improve recognition accuracy. Combined with the reading distance, the research presented in this paper can more accurately optimize the three-dimensional structure of the tag group, improve the reading performance of the tag group, and avoid the interference and collision of tags in the communication channel. Compared with the previous template matching algorithm, the tag group recognition ability put forward in this paper is improved by at least 13.9%, and its reading performance is improved by at least 6.2% as shown in many experiments.

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Section: Image Motion Deblurring Results and Analysismentioning

confidence: 99%

Metrology and Measurement Systems

Li,

Yu,

Liu

et al. 2021

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“…Recent approaches on image dehazing is mostly based on artificial intelligence approaches which mostly use deep learning models [23][24][25]. In [26] a deep architecture is developed by using CNN (Convolutional Neural Network) and a new unit called "bilateral rectified linear unit" is added to the neural network.…”

Section: Figure 2 Atmospheric Light Scattering Modelmentioning

confidence: 99%

Smart and real-time image dehazing on mobile devices

Çimtay

2021

J Real-Time Image Proc

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Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real-time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07s fps (frame per second) per frame processing time (~15fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.

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“…The final category, single image dehazing [7,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], has received the most attention over the last two decades due to its more realistic assumptions and wide application scenarios. Single image dehazing techniques can be further divided into two subclasses: one group of studies [15][16][17][18][19][20][21] attempt to achieve haze removal using image processing techniques, often employing a prior, while the other applies machine learning methods [22][23][24][25][26][27][28][29][30]. With the rapid development of artificial neural network, over the last five years, more and more convolutional neural network-based single image dehazing algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Improved single image dehazing methods for resource-constrained platforms

Yang

Evans

2021

J Real-Time Image Proc

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Image dehazing is an increasingly widespread approach to address the degradation of images of the natural environment by low-visibility weather, dust and other phenomena. Advances in autonomous systems and platforms have increased the need for low-complexity, high-performing dehazing techniques. However, while recent learning-based image dehazing approaches have significantly increased the dehazing performance, this has often been at the expense of complexity and hence the use of prior-based approaches persists, despite their lower performance. This paper addresses both these aspects and focuses on single image dehazing, the most practical class of techniques. A new Dark Channel Prior-based single image dehazing algorithm is presented that has an improved atmospheric light estimation method and a low-complexity morphological reconstruction. In addition, a novel, lightweight end-to-end network is proposed, that avoids information loss and significant computational effort by eliminating the pooling and fully connected layers. Qualitative and quantitative evaluations show that our proposed algorithms are competitive with, or outperform, state-of-the-art techniques with significantly lower complexity, demonstrating their suitability for use in resource-constrained platforms.

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Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

Cited by 22 publications

References 43 publications

Metrology and Measurement Systems

Metrology and Measurement Systems

Smart and real-time image dehazing on mobile devices

Improved single image dehazing methods for resource-constrained platforms

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