Image quality enhancement via gradient-limited random phase addition in holographic display

Zhao, Tao; Liu, Juan; Duan, Jing; Li, Xin; Wang, Yongtian

doi:10.1016/j.optcom.2019.02.026

Cited by 24 publications

(7 citation statements)

References 22 publications

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“…Since the random phase could lead to the excessive diffusion of information and degrades the image quality, the gradient-limited random phase (GL-RP) addition method and the limitedrandom-phase (L-RP) time average method have been developed to avoid excessive diffusion of the object information (Ma et al, 2017;Zhao et al, 2019). In the numeral simulation of the GL-RP method, the PSNR and SSIM of the reconstructed image increase by 10 and 13%, respectively.…”

Section: Non-iterative Methodsmentioning

confidence: 99%

Speckle Noise Suppression Algorithm of Holographic Display Based on Spatial Light Modulator

Chen

Lee

et al. 2022

Front. Photon.

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Holography has emerged as one of the most attractive three-dimensional display technologies. With the technological development of computer science and coherent light source, the computer-generated holography has been applied in many fields. However, the speckle noise of the holographic reconstructed image seriously affects the viewing experience. In this paper, the cause of speckle noise generation in holographic display is introduced. Then, discussions about the speckle noise suppression methods are provided. The discussions are categorized into iterative and non-iterative approaches. Besides, we also introduce other speckle noise suppression techniques which are proposed from the perspective of light coherence, human visual system and optical system. Finally, the prospects of different types of approaches are summarized.

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Section: Non-iterative Methodsmentioning

confidence: 99%

Speckle Noise Suppression Algorithm of Holographic Display Based on Spatial Light Modulator

Chen

Lee

et al. 2022

Front. Photon.

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“…In other words, it does not need RAP with 2π range as the initial phase to be added on the target image. Inspired by this idea, limited random phase method 79 , where RAP with 1.2π range is provided for the gray image and RAP with 1.5π range is added on the binary image, and gradient-limited random phase 80 , 81 , where RAP with different ranges is provided for the low frequency region and the high frequency region of the target image, were proposed successively. Furthermore, a frequency-based optimized random phase method 82 was also developed, where RAP with different frequency ranges and maximal values is added on different frequency regions of the target image with the help of the Butterworth filte.…”

Section: Image Quality Enhancement and Speckle Suppressionmentioning

confidence: 99%

Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display

2022

Self Cite

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Holographic three-dimensional display is an important display technique because it can provide all depth information of a real or virtual scene without any special eyewear. In recent years, with the development of computer and optoelectronic technology, computer-generated holograms have attracted extensive attention and developed as the most promising method to realize holographic display. However, some bottlenecks still restrict the development of computer-generated holograms, such as heavy computation burden, low image quality, and the complicated system of color holographic display. To overcome these problems, numerous algorithms have been investigated with the aim of color dynamic holographic three-dimensional display. In this review, we will explain the essence of various computer-generated hologram algorithms and provide some insights for future research.

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“…The gradient map of an image contains contour and edge information, which has strong sharpness and contrast. Some researchers have utilized the gradient information by different ways to enhance the image quality [34,35]. To get fused images with rich texture details and sharpened edges, an efficient approach by introducing the enhanced gradient information is presented to finish the fusion of detail layer, which is specifically introduced as follows.…”

Section: Fusion Of Detail Layermentioning

confidence: 99%

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

Han

Liu

et al. 2021

IEEE Access

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Infrared and visible image fusion is a hot topic due to the perfect complementarity of their information. There are two key problems in infrared and visible image fusion. One is how to extract significant target areas and rich texture details from the source images, and the other is how to integrate them to produce satisfactory fused images. To tackle these problems, we propose a novel fusion framework in this paper. A multi-level image decomposition method is used to obtain the base layer and detail layer of the source image. For the fusion of base layer, an ingenious fusion strategy guided by the saliency map of source image is designed to improve the intensity of salient targets and the visual quality of the fused image. For the fusion of detail layer, an efficient approach by introducing the enhanced gradient information is presented to boost the detail features and sharpen the edges of the fused image. Experimental results demonstrate that, compared with fifteen classical and advanced fusion methods, the proposed image fusion framework has better performance in both subjective and objective evaluation.

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Image quality enhancement via gradient-limited random phase addition in holographic display

Cited by 24 publications

References 22 publications

Speckle Noise Suppression Algorithm of Holographic Display Based on Spatial Light Modulator

Speckle Noise Suppression Algorithm of Holographic Display Based on Spatial Light Modulator

Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

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