WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, announced that it developed the holographic reconstruction network (HRNet) which has brought an important technological breakthrough in the field of hologram reconstruction. Holography has always played an important role in scientific research, medical imaging, industrial inspection and other fields. However, traditional hologram reconstruction methods face many challenges, such as the need for a priori knowledge, manual operation and complex post-processing steps. To address these problems, WiMi’s innovative technology, HRNet, which is based on deep learning and holographic image processing, has end-to-end hologram reconstruction capabilities without the need for a priori knowledge and complex post-processing steps. The technology breaks through the limitations of traditional holographic reconstruction methods, realizing noise-free image reconstruction and phase imaging, which brings great potential to image processing, computer vision and other related fields.
Holography is a technique that records the complete wavefront information of an object, including amplitude and phase. Conventional holographic reconstruction methods usually require a priori knowledge, such as object distance, angle of incidence, and wavelength, and require additional filtering operations to remove unwanted image information. In addition, phase imaging and processing of multi-section objects place higher demands on conventional methods. However, WiMi’s HRNet overcomes these challenges by employing an end-to-end learning strategy with deep learning, bringing an innovative solution to holographic reconstruction.
WiMi’s HRNet employs a deep learning approach to address some of the challenges faced by traditional methods. Some of the key aspects of the technology are described below:
End-to-end learning: HRNet uses an end-to-end learning strategy to learn and reconstruct directly from the original holograms. This means that the original hologram serves as input to the network without any prior knowledge or additional preprocessing steps.
Deep residual networks: The network architecture employs deep residual learning. This means adding identity mappings between network layers to simplify the training process and speed up computation. This approach helps to solve the problem of vanishing/exploding gradients in deep neural networks.
Noise-free reconstruction: HRNet is able to output noise-free reconstruction results, which means it can eliminate the problems caused by noise and distortion in traditional methods. This noise-free reconstruction helps to improve the quality and accuracy of reconstructed images.
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Phase imaging processing: HRNet can handle not only the reconstruction of amplitude objects, but also phase imaging. Conventional phase imaging requires compensation for phase aberration and additional unfolding steps to recover the true object thickness. HRNet is able to reconstruct phase information directly from holograms by learning the processing steps of phase imaging.
Multi-cross-section object processing: HRNet can also handle the reconstruction of multi-cross-section objects, extending the application’s degrees of freedom. This means that it is capable of generating full-focus images and depth maps, meeting the need for multi-dimensional data in many applications.
WiMi’s HRNet utilizes a deep learning and end-to-end learning approach to achieve noise-free image reconstruction by learning an internal representation of the holographic reconstruction that handles the needs of both phase imaging and multi-section objects. This data-driven approach eliminates the reliance on a priori knowledge and additional processing steps, providing a new and effective framework for digital holographic reconstruction.
The core of WiMi’s HRNet is to utilize the power of deep learning to reconstruct holograms without the need for any a priori knowledge or tedious pre-processing steps. This means that the original hologram serves as the input to the network, which automatically learns the necessary processing steps in holographic reconstruction and establishes pixel-level connections between the original hologram and backpropagation. This data-driven approach eliminates the reliance on a priori knowledge and additional processing steps, making the reconstruction process more efficient and accurate.
In HRNet, WiMi‘s research team used a deep residual learning approach to design the network architecture. This approach adds identity mapping between network layers, simplifying the training process and speeding up computation. This moderately deep network architecture is able to have sufficient fitting capability while avoiding excessive computational load, achieving a delicate balance between performance and training load. HRNet is able to output noise-free reconstruction results, which improves the quality and accuracy of the reconstructed images. This is important for many applications, especially for fields such as medical imaging, industrial inspection, and scientific research where high quality images are required. Noise and distortion are often one of the main reasons for the degradation of reconstructed image quality in traditional methods, while HRNet is able to eliminate these problems and provide noise-free reconstruction results through a deep learning approach.
SOURCE: PRNewswire
