A Fast Image Dehazing Algorithm Using Morphological Reconstruction

Salazar-Colores, Sebastián; Cabal-Yépez, Eduardo; Botella, Guillermo; Ledesma-Carrillo, Luis M.; Ledesma, Sergio

doi:10.1109/tip.2018.2885490

Cited by 95 publications

(61 citation statements)

References 29 publications

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“…A corpus of 100 and 500 random hazy images of real scenes are also used to verify the results of DCPWM. Some random hazy images are also taken from Fattal [4], He et al [7], Tarel and Hautiere [11], Kratz and Nishino [12], Meng et al [14], Cai et al [16], Yuan and Huang [26], Li et al [20], Salazar-Colores et al [18], Berman et al [19], and Ancuti et al [27] to compare DCPWM to the state-of-the-art methods.…”

Section: Experimental Results Evaluation Parameters and Discussionmentioning

confidence: 99%

“…e paper is a comparison between different state-of-the-art techniques and elaborates the advantages and disadvantages summarizing future scope as well. Salazar-Colores et al [18] proposed a fast technique using morphological operations for restoring quality images. e performance measure is based on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability‐Weighted Moments

Yousaf

Habib

Mehmood

et al. 2019

Mathematical Problems in Engineering

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e method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. e proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to stateof-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. e qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics.

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Section: Experimental Results Evaluation Parameters and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability‐Weighted Moments

Yousaf

Habib

Mehmood

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…However, the brightest pixel can represent a white object, a gray object in the scene, or an extra-light source, which leads to serious estimation errors. Despite the improvements done in several proposed works [35,36], the estimation of the atmospheric light still has significant errors in specific cases (hazy nighttime images).…”

Section: Atmospheric Light Estimationmentioning

confidence: 99%

“…The visual comparison employs the physical degradation model in Equation 1, where the transmission map is generated by using the conventional DCP approach [14]. [38]; (c) Salazar-Colores's method [35]; (d) Haouassi's method [36]; and (e) the proposed method.…”

mentioning

confidence: 99%

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

Haouassi

2020

Applied Sciences

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Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).

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“…According to the characteristics of bearing fault vibration and impact, the matching trace [17,18] defines atoms to disassemble the vibration signal and extract the impact characteristic components. In mathematical morphology analysis [19,20], the pre-defined structure operator is used to carry out corrosion, expansion, opening, and closing operations on the signal, such as to suppress the noise and In Figure 1 , , , , n x x x x  ). In the actual test process, when multiple sensors are used for observation, the number of sensors are generally required to be not less than the number of signal sources, that is, m n ≥ .…”

Section: Introductionmentioning

confidence: 99%

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Miao

Zhao

Wang

et al. 2020

Sensors

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During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments. extract the characteristic signal. Cyclostationary signal analysis [21,22] relies on the cyclostationary characteristics of the bearing fault impulse signal to design a filter to eliminate random noise. A Wiener filter [23,24] is used to eliminate the stationary random noise in the fault impulse signal. A wavelet transform [25,26] first decomposes the vibration signal into different frequency bands, then defines the threshold to eliminate the noise components and reconstructs the characteristic signal. A Kalman filter [27,28] first establishes the vibration state model and signal observation model of rolling bearing, then iteratively eliminates the noise in the signal. Stochastic resonance [29,30] uses noise to enhance the fault characteristic component of the bearing vibration signal. All of the above mentioned methods are universal, can effectively eliminate background noise and interference components, and extract fault signals in specific environment, but they are not suitable for complex interference situations, and especially when the interference components in vibration signals are similar to fault signals, it is difficult to distinguish them by the above method, let alone eliminating interference components and extract fault signals. Blind source separation (BSS) technology can realize the separation of multiple aliased signals [1,31]. Meanwhile, blind source separation is not affected by the time and frequency overlap of source signals, and the separated output signal will not lose the weak feature information in the source signal.So far, many effective and distinctive blind source separation algorithms have been constructed. Typical algorithms include fast fixed-point [32] algorithms, natural gradient [28] algorithms, second-order blind identification (SOBI) [33] algorithms, equivalation adaptive separation via independence (EASI) [34] algorithms, and joint approximate diagonalization of eigenmatrices (JADE) [35] algorithms. When separating the noiseless mixed signals, these ...

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A Fast Image Dehazing Algorithm Using Morphological Reconstruction

Cited by 95 publications

References 29 publications

Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability‐Weighted Moments

Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability‐Weighted Moments

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

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

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