Wavelet-based image fusion for securing multiple images through asymmetric keys

Mehra, Isha; Nishchal, Naveen K.

doi:10.1016/j.optcom.2014.09.040

Cited by 32 publications

(15 citation statements)

References 32 publications

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“…That is, time windows and frequency windows can have a dynamic adjustment, according to the specific signal forms. Due to the multi-resolution capability of the wavelet transform, the wavelet theory has been widely applied in the research of image processing such as image compression [19], image de-noising [20], image fusion [21] and so on.…”

Section: The Improved Wavelet Fuzzy Threshold De-noising Methodsmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

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Section: The Improved Wavelet Fuzzy Threshold De-noising Methodsmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

View full text Add to dashboard Cite

show abstract

“…Due to this reason, users are stuck with the basic key management functionality, which allows them to produce, delete and deliver the keys under the environment of network security. To solve this issue, an optical asymmetric cryptosystem has been proposed [19][20][21][22][23][24]. In an asymmetric cryptosystem, a pair of keys, an encryption key (public key) and a decryption key (private key) are entirely different functions.…”

Section: Introductionmentioning

confidence: 99%

“…Even if an opponent knew the encryption key and the corresponding ciphertext, it should still be hard to recover the input data. Thus, a central role that such asymmetric encryption system plays is an effective trapdoor oneway function [19].Employing different optical domains the security of asymmetric cryptosystem was further improved [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Optical asymmetric image encryption using gyrator wavelet transform

Mehra

Nishchal

2015

Optics Communications

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“…Using infrared and visible image fusion technique, we not only can overcome the limitation of information obtained from individual sensors but also can achieve improved observation. Therefore, infrared and visible image fusion is an important technique to enhance the value of the image fusion technique in various fields, such as object detection [4], object tracking [5], face recognition [6], hiding [7], and securing multiple images [8].…”

Section: Introductionmentioning

confidence: 99%

Infrared and visible image fusion with spectral graph wavelet transform

Yan

Qin

et al. 2015

J. Opt. Soc. Am. A

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Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

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Wavelet-based image fusion for securing multiple images through asymmetric keys

Cited by 32 publications

References 32 publications

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Optical asymmetric image encryption using gyrator wavelet transform

Infrared and visible image fusion with spectral graph wavelet transform

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