Automatic detection and removal of high‐density impulse noises

Tian, Bangsen; Tan, Jieqing

doi:10.1049/iet-ipr.2014.0286

Cited by 24 publications

(15 citation statements)

References 22 publications

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“…Our computed AUC is 99.8, which is 4.909%, 0.4%, 0.2%, 0.3%, 2.78%, 0.3%, and 1.2% more compared to existing approaches LMVD [34], MDT [35], HOG+HOS [36], LS [37], SR [38], CI [39] and SFM [40].…”

Section: Results Analysismentioning

confidence: 80%

“…Fig 5, fig 6, fig 7 and, fig 8 shows the frame level comparison that corresponds to scenario-A, scenario-B, scenario-C and scenario-D. Finally, we computed the Area Under Curve (AUC) and compared with the existing approach as given in fig 9, where our proposed DCAD model is compared with LMVD [34], MDT [35], HOG+HOS [36], LS [37], SR [38], CI [39] and SFM [40].…”

Section: Results Analysismentioning

confidence: 99%

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Distribution Based Crowd Abnormality Detection

C*,

Ramesh

2019

IJITEE

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The complications of abnormal behavior and behavior identification are very eminent problems in the video processing. Abnormal behavior detector can be designed by choosing the region of interest through feature detector and by tracking them over the short time period. Therefore, the detector shows the trade-off among the object tracking and optical flow. Since, various regions normally display the various types of motion pattern, we introduce Distribution Based Crowd Abnormality Detection (DCAD) which catches the statistics of object trajectories which are passing via the Spatio-temporal cube. This technique directly provides the distribution to define the frame. Also clustering is not required to build the dictionary. Besides, we exploited the motion trajectories to calculate the “power potentials” in the pixel space which defines the amount of interaction among the people. Furthermore, utilize the standard method for classification by considering SVMs (Support Vector Machines) discriminative learning method to recognize the abnormalities.

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Section: Results Analysismentioning

confidence: 80%

Section: Results Analysismentioning

confidence: 99%

Distribution Based Crowd Abnormality Detection

C*,

Ramesh

2019

IJITEE

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“…Our approach was based on the observation that the texture details of repaired images by most image inpainting algorithms were not prominent. Inspired by the applications of continued fractions in image processing [11,[18][19][20], we proposed a novel image inpainting algorithm by using continued fractions rational interpolation. In order to obtain better repaired results, Thiele's rational interpolation was combined with Newton-Thiele's rational interpolation to repair damaged images.…”

Section: Discussionmentioning

confidence: 99%

“…Considering that the reconstruction images have better visual effect and prominent texture by the continued fractions [11,[18][19][20], in this paper, we propose an adaptive image inpainting method based on continued fractions.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Image Inpainting Method Based on Continued Fractions Interpolation

Xing

Xia

et al. 2018

Discrete Dynamics in Nature and Society

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In view of the drawback of most image inpainting algorithms by which texture was not prominent, an adaptive inpainting algorithm based on continued fractions was proposed in this paper. In order to restore every damaged point, the information of known pixel points around the damaged point was used to interpolate the intensity of the damaged point. The proposed method included two steps; firstly, Thiele's rational interpolation combined with the mask image was used to interpolate adaptively the intensities of damaged points to get an initial repaired image, and then Newton-Thiele's rational interpolation was used to refine the initial repaired image to get a final result. In order to show the superiority of the proposed algorithm, plenty of experiments were tested on damaged images. Subjective evaluation and objective evaluation were used to evaluate the quality of repaired images, and the objective evaluation was comparison of Peak Signal to Noise Ratios (PSNRs). The experimental results showed that the proposed algorithm had better visual effect and higher Peak Signal to Noise Ratio compared with the state-of-the-art methods.

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“…Some other remarkable model proposed included [11] which presents particle swarm optimisation (PSO) to optimise the parameters necessitated by the fuzzy‐based filter while [12] proposed a low computational complexity interpolation‐based impulse noise removal. Bai and Tan [13] proposed a two‐stage denoiser for high‐density impulse noise images based on local mean and variance detector (LMVD) with Newton–Thiele filter (NTF), which can automatically identify the corrupted images before further processing.…”

Section: Introductionmentioning

confidence: 99%