Deep Learning CT Image Restoration using System Blur and Noise Models

• URL: http://arxiv.org/abs/2407.14983v1
• Date: Sat, 20 Jul 2024 21:17:35 GMT
• Title: Deep Learning CT Image Restoration using System Blur and Noise Models
• Authors: Yijie Yuan, Grace J. Gang, J. Webster Stayman,
• Abstract summary: We present a method that leverages both degraded image inputs and a characterization of the system blur and noise to combine modeling and deep learning approaches. Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics.
• Score: 2.2530496464901106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities like computed tomography (CT). Although the blur and noise in CT images can be attributed to a variety of system factors, these image properties can often be modeled and predicted accurately and used in classical restoration approaches for deconvolution and denoising. In classical approaches, simultaneous deconvolution and denoising can be challenging and often represent competing goals. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. In this work, we present a method that leverages both degraded image inputs and a characterization of the system blur and noise to combine modeling and deep learning approaches. Different methods to integrate these auxiliary inputs are presented. Namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any CNN architecture. The proposed model shows superior performance compared to baseline models lacking auxiliary inputs. Evaluations are based on the average Peak Signal-to-Noise Ratio (PSNR), selected examples of good and poor performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.

Related papers

• Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment [82.13830107682232]
  We propose a novel class of state-of-the-art (SOTA) generative model, which exhibits the capability to model intricate relationships. We devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images. Two visual evaluation branches are designed to comprehensively analyze the obtained high-level feature information.
  arXiv  Detail & Related papers  (2024-02-22T09:39:46Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Anomaly Detection with Conditioned Denoising Diffusion Models [32.37548329437798]
  We introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image. Our anomaly detection framework employs the conditioning mechanism, where the target image is set as the input image to guide the denoising process. DDAD achieves state-of-the-art results of (99.8 %) and (98.9 %) image-level AUROC respectively.
  arXiv  Detail & Related papers  (2023-05-25T11:54:58Z)
• Masked Image Training for Generalizable Deep Image Denoising [53.03126421917465]
  We present a novel approach to enhance the generalization performance of denoising networks. Our method involves masking random pixels of the input image and reconstructing the missing information during training. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.
  arXiv  Detail & Related papers  (2023-03-23T09:33:44Z)
• CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser [7.442030347967277]
  existing plug-and-play image restoration methods are designed for non-blind denoising. We propose a novel framework with blind prior, which can deal with more complicated image restoration problems in the real world. Our model is shown to be able to recover the fine image details tiny structures even when the noise level is different.
  arXiv  Detail & Related papers  (2022-11-14T11:30:24Z)
• Deep Learning-Based Defect Classification and Detection in SEM Images [1.9206693386750882]
  In particular, we train RetinaNet models using different ResNet, VGGNet architectures as backbone. We propose a preference-based ensemble strategy to combine the output predictions from different models in order to achieve better performance on classification and detection of defects.
  arXiv  Detail & Related papers  (2022-06-20T16:34:11Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders [81.30960319178725]
  We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs) First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
  arXiv  Detail & Related papers  (2020-06-10T21:28:13Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.