Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

• URL: http://arxiv.org/abs/2208.03008v1
• Date: Fri, 5 Aug 2022 06:54:44 GMT
• Title: Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
• Authors: Yongsong Huang, Qingzhong Wang, Shinichiro Omachi
• Abstract summary: We present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Our proposed method achieves $31.90$ of PSNR with a scale factor of $4 times$, which is $7.05 %$ higher than that obtained by recent work.
• Score: 9.599347633285635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves $31.90$ of PSNR with a scale factor of $4 \times$, which is $7.05 \%$ higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.

Related papers

• Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer [8.009052363001903]
  High-resolution radiographic images play a pivotal role in the early diagnosis and treatment of skeletal muscle-related diseases. It is promising to enhance image quality by introducing single-image super-resolution (SISR) model into the radiology image field. The conventional image pipeline, which can learn a mixed mapping between SR and denoising from the color space and inter-pixel patterns, poses a particular challenge for radiographic images with limited pattern features.
  arXiv  Detail & Related papers  (2023-12-27T07:56:24Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution [2.647302105102753]
  Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is a practical model for recovering HR images from real-world LR images. We employ the high-order degradation model of the Real-ESRGAN which better simulates real-world image degradations. The proposed model achieves superior perceptual quality compared to the Real-ESRGAN model, effectively preserving fine details and generating images with more realistic textures.
  arXiv  Detail & Related papers  (2022-11-01T16:48:04Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Single MR Image Super-Resolution using Generative Adversarial Network [0.696125353550498]
  Real Enhanced Super Resolution Generative Adrial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images. In this paper, we apply this method to enhance the spatial resolution of 2D MR images.
  arXiv  Detail & Related papers  (2022-07-16T23:15:10Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss [3.4358954898228604]
  Super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. We proposed a novel super-resolution method that uses a generative adversarial network (GAN) with cyclic loss and attention mechanism.
  arXiv  Detail & Related papers  (2021-07-21T10:07:22Z)
• Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution [65.25508348574974]
  We go below the MRI acceleration factors reported by all published papers that reference the original fastMRI challenge. We consider powerful deep learning based image enhancement methods to compensate for the underresolved images. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor.
  arXiv  Detail & Related papers  (2021-03-04T10:45:01Z)
• Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN [63.49373689654419]
  We propose a new perception consistency ultrasound image super-resolution (SR) method based on self-supervision and cycle generative adversarial network (CycleGAN) We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement. We then make full use of the cycle loss of LR-SR-LR and HR-LR-SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results.
  arXiv  Detail & Related papers  (2020-12-28T08:24:04Z)

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.