Reconstruction of Optical Coherence Tomography Images from Wavelength-space Using Deep-learning

• URL: http://arxiv.org/abs/2509.18783v1
• Date: Tue, 23 Sep 2025 08:21:53 GMT
• Title: Reconstruction of Optical Coherence Tomography Images from Wavelength-space Using Deep-learning
• Authors: Maryam Viqar, Erdem Sahin, Elena Stoykova, Violeta Madjarova,
• Abstract summary: We propose a streamlined and computationally efficient approach to reconstruct speckle-reduced OCT images directly from the wavelength domain.<n>For reconstruction, two encoder-decoder styled networks namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN) are used.<n>We quantitatively and visually demonstrate the efficacy of the method in obtaining high-quality OCT images.
• Score: 1.1549572298362782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional Fourier-domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low coherence interferometry. We propose a streamlined and computationally efficient approach based on Deep-Learning (DL) which enables reconstructing speckle-reduced OCT images directly from the wavelength domain. For reconstruction, two encoder-decoder styled networks namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN) are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in Fourier domain (FD). We quantitatively and visually demonstrate the efficacy of the method in obtaining high-quality OCT images. Furthermore, we illustrate the computational complexity reduction by harnessing the power of DL models. We believe that this work lays the framework for further innovations in the realm of OCT image reconstruction.

Related papers

• Resolution-Independent Neural Operators for Multi-Rate Sparse-View CT [67.14700058302016]
  Deep learning methods achieve high-fidelity reconstructions but often overfit to a fixed acquisition setup.<n>We propose Computed Tomography neural Operator (CTO), a unified CT reconstruction framework that extends to continuous function space.<n>CTO enables consistent multi-sampling-rate and cross-resolution performance, with on average >4dB PSNR gain over CNNs.
  arXiv  Detail & Related papers  (2025-12-13T08:31:46Z)
• Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding [50.54887630778593]
  Compressive imaging (CI) reconstruction aims to recover high-dimensional images from low-dimensional measurements compressed.<n>Existing unsupervised representations may struggle to achieve a desired balance between representation ability and efficiency.<n>We propose Decomposed multi-resolution Grid encoding (GridTD), an unsupervised continuous representation framework for CI reconstruction.
  arXiv  Detail & Related papers  (2025-07-10T12:36:20Z)
• WAVE-UNET: Wavelength based Image Reconstruction method using attention UNET for OCT images [1.0835264351334324]
  We propose a systematic design methodology WAVE-UNET to reconstruct the high-quality OCT images directly from the lambda-space to reduce the complexity. This framework uses modified UNET having attention gating and residual connections, with IDFT processed lambda-space fringes as the input. The method consistently outperforms the traditional OCT system by generating good-quality B-scans with highly reduced time-complexity.
  arXiv  Detail & Related papers  (2024-10-05T11:16:10Z)
• UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction [0.0]
  Compton scatter tomography (CST) presents an interesting alternative to conventional CT. Deep unrolling networks have demonstrated potential in CT image reconstruction. UnWave-Net is a novel unrolled wavelet-based reconstruction network.
  arXiv  Detail & Related papers  (2024-06-05T16:10:29Z)
• Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet) [14.146848823672677]
  We introduce a unified complex-valued deep learning framework-Artificial Fourier Transform Network (AFTNet)<n>AFTNet can be readily used to solve image inverse problems in domain transformation.<n>We show that AFTNet achieves superior accelerated MRI reconstruction compared to existing approaches.
  arXiv  Detail & Related papers  (2023-12-18T02:50:45Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement [1.2645663389012574]
  Low-light image enhancement is a classical computer vision problem aiming to recover normal-exposure images from low-light images. convolutional neural networks commonly used in this field are good at sampling low-frequency local structural features in the spatial domain. We propose a novel module using the Fourier coefficients, which can recover high-quality texture details under the constraint of semantics in the frequency phase.
  arXiv  Detail & Related papers  (2022-09-16T13:56:09Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• u-net CNN based fourier ptychography [5.46367622374939]
  We propose a new retrieval algorithm that is based on convolutional neural networks. Experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
  arXiv  Detail & Related papers  (2020-03-16T22:48:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.