Detection and removal of Salt and Pepper noise in images by improved median filter

Deivalakshmi, S.; Sarath, S; Palanisamy, P.

doi:10.1109/raics.2011.6069335

Cited by 24 publications

(9 citation statements)

References 6 publications

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“…We use a third, unlit image (not shown) to allow us to account for ambient illumination. The ambient removal tends to produce high levels of salt-and-pepper noise in background regions, as evidenced in (c), which is readily isolated using methods from the literature, 33 providing the result shown in (d). Note the fine-grained of wrinkles in the clothing.…”

Section: Object Detectionmentioning

confidence: 98%

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Long-range concealed object detection through active covert illumination

et al. 2015

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When capturing a scene for surveillance, the addition of rich 3D data can dramatically improve the accuracy of object detection or face recognition. Traditional 3D techniques, such as geometric stereo, only provide a coarse grained reconstruction of the scene and are ill-suited to fine analysis. Photometric stereo is a well established technique providing dense, high-resolution, reconstructions, using active artificial illumination of an object from multiple directions to gather surface information. It is typically used indoors, at short range (<1 m), under highly controlled lighting conditions, and so has rarely found application in real-world, outdoor surveillance. One difficulty in such scenes is that the near infrared light generally used to illuminate a scene is overwhelmed by ambient illumination from the sun, and so it is not possible to reliably discriminate the artificial light from the ambient. This work aims to overcome this limitation and counteract the effect of sunlight by exploiting atmospheric absorption at a particular wavelength, 940 nm, in combination with image capture using state-of-the-art black silicon sensors. We first show that this combination, together with low-cost, focused LED illuminators, allows us to reliably differentiate artificial illumination from ambient lighting. We then extend our system to perform photometric stereo reconstruction at distances far beyond the current state of the art under direct sunlight, with a possibility for extending the range to 100 m and beyond. Finally, we provide some example use cases for the photometric stereo data in the form of detection of concealed objects and face recognition.

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Section: Object Detectionmentioning

confidence: 98%

“…An unfortunate side-effect of ambient light removal is that salt-and-pepper image noise is emphasised in the background after calculation of the surface gradients, as shown in Figure 13. Such noise can be readily isolated and masked using the method of Deivalakshmi et al, 33 or similar.…”

Section: Object Detectionmentioning

confidence: 99%

Long-range concealed object detection through active covert illumination

et al. 2015

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“…As far as the watermarking process is concerned, it is useful to check the effects of both the additive noise and the removal process on our data hiding process. Many methods were proposed to eliminate the noise or enhance the visual appearance of the images [18,19]; these methods depend mainly on the idea of median filters. The normal median filters, for example, which are used to eliminate the salt-and-pepper noise in images, do in fact filter the whole image regardless of the presence or absence of the noise in a certain area.…”

Section: Noise Removal Selective Filtermentioning

confidence: 99%

DWT-Based Data Hiding Technique for Videos Ownership Protection

Alenizi¹,

Al-Asmari²

2019

Wavelet Transform and Complexity

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This chapter proposes a wavelet data hiding scheme for video authentication and ownership protection. A watermark in the shape of a logo image will be hidden. In this research, a discrete wavelet transform (DWT) process is implemented using orthonormal filter banks, where the Y components of the YUV color space of the video frames are decomposed using DWT, and a watermark is inserted in one or more of the resulting sub-bands in a way that is fully controlled by the owner. Then, the watermarked video is reconstructed. The filters used for the DWT decompositions are randomly generated to increase the security of the algorithm. An enhanced detection technique is developed to increase the reliability of the system. The overall robustness of this scheme is measured when common attacks are applied to the test videos. Moreover, the proposed algorithm is used with the high-efficiency video coding (HEVC) technique to examine the whole performance. Furthermore, a selective denoising filter is built to eliminate the effect of the noise. The simulation results show that the proposed algorithm achieves well under both the visual and the metric tests. Moreover, it performed well against intentional and unintentional attacks. The average normalized correlation achieved is 97%, while the mean peak signal-to-noise ratio (PSNR) is 45 dB.

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“…Thus, it gives the optimal hyperplane with small training sets and high accuracy results. The SVM solves both the linear and nonlinear problems [9]. Basically, the SVM maps the original data from the input space into higher dimensional feature space so that it can easily separate the two classes.…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…Several studies have reported that SVM (Support Vector Machine) is one of the most prominent algorithm in the data mining area, delivers higher performance in terms of classification and object detection as well [1,9]. However, here the SVM acts as a detecting algorithm which is used for solving the purpose of detection of noise type from an image.…”

Section: Introductionmentioning

confidence: 99%