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

Li, Nannan; Chen, Chun; Lee, Byoungho; Wang, Di; Wang, Qiong‐Hua

doi:10.3389/fphot.2021.825610

Cited by 9 publications

(3 citation statements)

References 68 publications

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“…Numerical reconstruction is performed to verify the validity of our method. The reconstructed image quality by the proposed method is similar to that by SGD in terms of peak-signal-noise-ratio (PSNR) and similarity index measure (SSIM) [12], while the reconstructed images show less speckle noise than SGD due to the smooth phase distribution in the reconstructed plane [13]. Moreover, the proposed method can infer a hologram about 100 times faster than SGD (about 13s for SGD and 0.13s for proposed method respectively)…”

Section: Objective and Backgroundmentioning

confidence: 74%

49.3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography

Dong

Chen

et al. 2023

Symp Digest of Tech Papers

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Section: Objective and Backgroundmentioning

confidence: 74%

49.3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography

Dong

Chen

et al. 2023

Symp Digest of Tech Papers

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“…Therefore, SAR exhibits excellent penetration capability and can acquire high-quality remote sensing data in adverse weather conditions such as clouds, haze, rain, or snow, making it an all-weather remote sensing technology [2][3][4][5][6]. However, in the process of SAR imaging, the generation of speckle noise is inevitable [7], which significantly affects the quality and resolution of SAR images [8]. To overcome this issue, there are currently two main strategies:…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Integrated Speckle Reduction and Super-Resolution in Multi-Temporal SAR

Bu,

Zhang,

Zhang

et al. 2023

Remote Sensing

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In the domain of synthetic aperture radar (SAR) image processing, a prevalent issue persists wherein research predominantly focuses on single-task learning, often neglecting the concurrent impact of speckle noise and low resolution on SAR images. Currently, there are two main processing strategies. The first strategy involves conducting speckle reduction and super-resolution processing step by step. The second strategy involves performing speckle reduction as an auxiliary step, with a focus on enhancing the primary task of super-resolution processing. However, both of these strategies exhibit clear deficiencies. Nevertheless, both tasks jointly focus on two key aspects, enhancing SAR quality and restoring details. The fusion of these tasks can effectively leverage their task correlation, leading to a significant improvement in processing effectiveness. Additionally, multi-temporal SAR images covering imaging information from different time periods exhibit high correlation, providing deep learning models with a more diverse feature expression space, greatly enhancing the model’s ability to address complex issues. Therefore, this study proposes a deep learning network for integrated speckle reduction and super-resolution in multi-temporal SAR (ISSMSAR). The network aims to reduce speckle in multi-temporal SAR while significantly improving the image resolution. Specifically, it consists of two subnetworks, each taking the SAR image at time 1 and the SAR image at time 2 as inputs. Each subnetwork includes a primary feature extraction block (PFE), a high-level feature extraction block (HFE), a multi-temporal feature fusion block (FFB), and an image reconstruction block (REC). Following experiments on diverse data sources, the results demonstrate that ISSMSAR surpasses speckle reduction and super-resolution methods based on a single task in terms of both subjective perception and objective evaluation metrics regarding the quality of image restoration.

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“…Several related studies predominantly focus on technical noise, such as photon noise and quantization noise, and coherent noise, such as speckle noise [12][13][14]. The authors in [8] have shown that deep neural networks are susceptible to white Gaussian noise.…”

mentioning

confidence: 99%

Hologram Noise Model for Data Augmentation and Deep Learning

Terbe,

Orzó,

Bicsák

et al. 2024

Sensors

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This paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems. Our method, demonstrated through the classification of digital holographic images, utilizes a novel approach to synthesize and apply random colored noise, addressing the typically encountered correlated noise patterns in such images. Empirical results show that our technique not only maintains classification accuracy in high-quality images but also significantly improves it when given noisy inputs without increasing the training time. This advancement demonstrates the potential of our approach for augmenting data for deep learning models to perform effectively in production under varied and suboptimal conditions.

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

Cited by 9 publications

References 68 publications

49.3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography

49.3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography

Deep Learning for Integrated Speckle Reduction and Super-Resolution in Multi-Temporal SAR

Hologram Noise Model for Data Augmentation and Deep Learning

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