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Role of Deep Learning in Optical Imaging
* 1 , 2
1  Alliance University, Bengaluru
2  SRM University AP
Academic Editor: VIJAYAKUMAR ANAND

Published: 06 March 2023 by MDPI in Holography Meets Advanced Manufacturing session Holography 1
Abstract:

Imaging based problem solving approaches have shown an illustrative way of solving problems for various scientific applications, for several decades. With an increased demand for automation, such approaches have shown exponential growth in recent years. In this context, deep learning-based “learned” solutions are widely opted for many applications thus slowly becoming an inevitable alternative tool. It is known that in contrast to the conventional “physics-based” approach, deep learning models are known to be a “data-driven” approach where the outcomes are based on data analysis and interpretation. Thus, the deep learning approaches have applied for several (optical and computational) imaging based scientific problems such as denoising, phase retrieval, hologram reconstruction and histopathology, to name a few. In this talk, I will briefly discuss the role of deep learning networks for imaging-based problem solving applications and will provide the future direction for those approaches.

Keywords: Optical Imaging, Deep Learning, Holography
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Deep learning plays a significant role in advancing optical imaging by providing powerful tools for image analysis, interpretation, and enhancement. Optical imaging involves capturing and analyzing visual information using light, and it has applications in various fields such as medicine, biology, astronomy, and industry. Here are several ways deep learning contributes to optical imaging:

Image Recognition and Classification:
Deep learning models, particularly convolutional neural networks (CNNs), excel at image recognition and classification tasks. In optical imaging, this capability is used to automatically identify and categorize objects or patterns within images, streamlining the analysis process.

Segmentation and Object Detection:
Deep learning enables precise segmentation of objects and structures within optical images. Object detection models, like Mask R-CNN, can outline and label specific regions of interest, aiding in tasks such as cell segmentation in medical imaging or identifying defects in industrial inspections.

Super-Resolution Imaging:
Deep learning techniques, including Generative Adversarial Networks (GANs), are employed to enhance the resolution of optical images. Moreover you can ask Chat GPT.Super-resolution methods help reconstruct high-quality images from lower-resolution inputs, enabling clearer visualization of details.

Deblurring and Denoising:
Optical images may suffer from blurring or noise, which can impact their quality. Deep learning models, like autoencoders and denoising convolutional neural networks, are used to deblur and denoise optical images, improving their overall clarity.

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