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Xianzhong Chen – One of the best experts on this subject based on the ideXlab platform.

  • high resolution Grayscale Image hidden in a laser beam
    Light-Science & Applications, 2018
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polapolarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics. A technique for encoding Images in the polarization distribution of a light beam has been demonstrated by a team in the UK and China. Conventionally, black and white Images are generated by creating optical patterns in which the light intensity varies with position. Now, Shuang Zhang at the University of Birmingham in the UK, Xianzhong Chen at the Heriot-Watt University in the UK and co-workers have produced Grayscale Images by spatially varying the polarization in a light beam rather than the light intensity. Since the light beam has a uniform intensity, the Images are invisible to the eye until a polarizer is inserted into the beam, thus making the method potentially useful for optical Image encryption. The researchers generated an Image of the famous physicist James Maxwell by using a reflective metasurface to alter the polarization of a reflected laser beam.

  • High-resolution Grayscale Image hidden in a laser beam
    Light science & applications, 2017
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polapolarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics.

Danchen Wang – One of the best experts on this subject based on the ideXlab platform.

  • encryption of qr code and Grayscale Image in interference based scheme with high quality retrieval and silhouette problem removal
    Optics and Lasers in Engineering, 2016
    Co-Authors: Hongjuan Wang, Zhipeng Wang, Qiong Gong, Danchen Wang
    Abstract:

    Abstract In optical interference-based encryption (IBE) scheme, the currently available methods have to employ the iterative algorithms in order to encrypt two Images and retrieve cross-talk free decrypted Images. In this paper, we shall show that this goal can be achieved via an analytical process if one of the two Images is QR code. For decryption, the QR code is decrypted in the conventional architecture and the decryption has a noisy appearance. Nevertheless, the robustness of QR code against noise enables the accurate acquisition of its content from the noisy retrieval, as a result of which the primary QR code can be exactly regenerated. Thereafter, a novel optical architecture is proposed to recover the Grayscale Image by aid of the QR code. In addition, the proposal has totally eliminated the silhouette problem existing in the previous IBE schemes, and its effectiveness and feasibility have been demonstrated by numerical simulations.

  • encryption of qr code and Grayscale Image in interference based scheme with high quality retrieval and silhouette problem removal
    Optics and Lasers in Engineering, 2016
    Co-Authors: Yi Qin, Hongjuan Wang, Zhipeng Wang, Qiong Gong, Danchen Wang
    Abstract:

    Abstract In optical interference-based encryption (IBE) scheme, the currently available methods have to employ the iterative algorithms in order to encrypt two Images and retrieve cross-talk free decrypted Images. In this paper, we shall show that this goal can be achieved via an analytical process if one of the two Images is QR code. For decryption, the QR code is decrypted in the conventional architecture and the decryption has a noisy appearance. Nevertheless, the robustness of QR code against noise enables the accurate acquisition of its content from the noisy retrieval, as a result of which the primary QR code can be exactly regenerated. Thereafter, a novel optical architecture is proposed to recover the Grayscale Image by aid of the QR code. In addition, the proposal has totally eliminated the silhouette problem existing in the previous IBE schemes, and its effectiveness and feasibility have been demonstrated by numerical simulations.

Xiaofei Zang – One of the best experts on this subject based on the ideXlab platform.

  • high resolution Grayscale Image hidden in a laser beam
    Light-Science & Applications, 2018
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics. A technique for encoding Images in the polarization distribution of a light beam has been demonstrated by a team in the UK and China. Conventionally, black and white Images are generated by creating optical patterns in which the light intensity varies with position. Now, Shuang Zhang at the University of Birmingham in the UK, Xianzhong Chen at the Heriot-Watt University in the UK and co-workers have produced Grayscale Images by spatially varying the polarization in a light beam rather than the light intensity. Since the light beam has a uniform intensity, the Images are invisible to the eye until a polarizer is inserted into the beam, thus making the method potentially useful for optical Image encryption. The researchers generated an Image of the famous physicist James Maxwell by using a reflective metasurface to alter the polarization of a reflected laser beam.

  • High-resolution Grayscale Image hidden in a laser beam
    Light science & applications, 2017
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics.

Fuyong Yue – One of the best experts on this subject based on the ideXlab platform.

  • high resolution Grayscale Image hidden in a laser beam
    Light-Science & Applications, 2018
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics. A technique for encoding Images in the polarization distribution of a light beam has been demonstrated by a team in the UK and China. Conventionally, black and white Images are generated by creating optical patterns in which the light intensity varies with position. Now, Shuang Zhang at the University of Birmingham in the UK, Xianzhong Chen at the Heriot-Watt University in the UK and co-workers have produced Grayscale Images by spatially varying the polarization in a light beam rather than the light intensity. Since the light beam has a uniform intensity, the Images are invisible to the eye until a polarizer is inserted into the beam, thus making the method potentially useful for optical Image encryption. The researchers generated an Image of the famous physicist James Maxwell by using a reflective metasurface to alter the polarization of a reflected laser beam.

  • High-resolution Grayscale Image hidden in a laser beam
    Light science & applications, 2017
    Co-Authors: Fuyong Yue, Chunmei Zhang, Xiaofei Zang, Dandan Wen, Brian D. Gerardot, Shuang Zhang, Xianzhong Chen
    Abstract:

    Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles. In addition to the intensity and color, the information of an Image can be encoded in a spatially varying distribution of phase or polarization state. Interestingly, such Images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles. Here, we propose and experimentally demonstrate an approach to hide a high-resolution Grayscale Image in a square laser beam with a size of less than half a millimeter. An Image with a pixel size of 300 × 300 nm is encoded into the spatially variant polarization states of the laser beam, which can be revealed after passing through a linear polarizer. This unique technology for hiding Grayscale Images and polarization manipulation provides new opportunities for various applications, including encryption, imaging, optical communications, quantum science and fundamental physics.

Jaegul Choo – One of the best experts on this subject based on the ideXlab platform.

  • ECCV (12) – Coloring with Words: Guiding Image Colorization Through Text-Based Palette Generation
    Computer Vision – ECCV 2018, 2018
    Co-Authors: Hyojin Bahng, Wonwoong Cho, David Keetae Park, Seungjoo Yoo, Jaegul Choo
    Abstract:

    This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given Grayscale Image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. For this task, we introduce our manually curated dataset called Palette-and-Text (PAT). Our proposed model called Text2Colors consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former captures the semantics of the text input and produce relevant color palettes. The latter colorizes a Grayscale Image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an Image.

  • Text2Colors: Guiding Image Colorization through Text-Driven Palette Generation.
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wonwoong Cho, Hyojin Bahng, David Keetae Park, Seungjoo Yoo, Jaegul Choo
    Abstract:

    In this paper, we propose a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given Grayscale Image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. To achieve this task, we introduce our manually curated dataset called Palette-and-Text (PAT), which consists of 10,183 pairs of text and its corresponding color palette. Our proposed model consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former employs a sequence-to-sequence model with an attention module to capture the semantics of the text input and produce relevant color palettes. The latter utilizes a U-Net architecture to colorize a Grayscale Image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an Image.

  • Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Hyojin Bahng, Wonwoong Cho, David Keetae Park, Seungjoo Yoo, Jaegul Choo
    Abstract:

    This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given Grayscale Image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. For this task, we introduce our manually curated dataset called Palette-and-Text (PAT). Our proposed model called Text2Colors consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former captures the semantics of the text input and produce relevant color palettes. The latter colorizes a Grayscale Image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an Image.