2,081 research outputs found
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans
CT scanners that are commonly-used in hospitals nowadays produce
low-resolution images, up to 512 pixels in size. One pixel in the image
corresponds to a one millimeter piece of tissue. In order to accurately segment
tumors and make treatment plans, doctors need CT scans of higher resolution.
The same problem appears in MRI. In this paper, we propose an approach for the
single-image super-resolution of 3D CT or MRI scans. Our method is based on
deep convolutional neural networks (CNNs) composed of 10 convolutional layers
and an intermediate upscaling layer that is placed after the first 6
convolutional layers. Our first CNN, which increases the resolution on two axes
(width and height), is followed by a second CNN, which increases the resolution
on the third axis (depth). Different from other methods, we compute the loss
with respect to the ground-truth high-resolution output right after the
upscaling layer, in addition to computing the loss after the last convolutional
layer. The intermediate loss forces our network to produce a better output,
closer to the ground-truth. A widely-used approach to obtain sharp results is
to add Gaussian blur using a fixed standard deviation. In order to avoid
overfitting to a fixed standard deviation, we apply Gaussian smoothing with
various standard deviations, unlike other approaches. We evaluate our method in
the context of 2D and 3D super-resolution of CT and MRI scans from two
databases, comparing it to relevant related works from the literature and
baselines based on various interpolation schemes, using 2x and 4x scaling
factors. The empirical results show that our approach attains superior results
to all other methods. Moreover, our human annotation study reveals that both
doctors and regular annotators chose our method in favor of Lanczos
interpolation in 97.55% cases for 2x upscaling factor and in 96.69% cases for
4x upscaling factor.Comment: Accepted in IEEE Acces
Generative Prior for Unsupervised Image Restoration
The challenge of restoring real world low-quality images is due to a lack of appropriate training data and difficulty in determining how the image was degraded. Recently, generative models have demonstrated great potential for creating high- quality images by utilizing the rich and diverse information contained within the model’s trained weights and learned latent representations. One popular type of generative model is the generative adversarial network (GAN). Many new methods have been developed to harness the information found in GANs for image manipulation. Our proposed approach is to utilize generative models for both understanding the degradation of an image and restoring it. We propose using a combination of cycle consistency losses and self-attention to enhance face images by first learning the degradation and then using this information to train a style-based neural network. We also aim to use the latent representation to achieve a high level of magnification for face images (x64). By incorporating the weights of a pre-trained StyleGAN into a restoration network with a vision transformer layer, we hope to improve the current state-of-the-art in face image restoration. Finally, we present a projection-based image-denoising algorithm named Noise2Code in the latent space of the VQGAN model with a fixed-point regularization strategy. The fixed-point condition follows the observation that the pre-trained VQGAN affects the clean and noisy images in a drastically different way. Unlike previous projection-based image restoration in the latent space, both the denoising network and VQGAN model parameters are jointly trained, although the latter is not needed during the testing. We report experimental results to demonstrate that the proposed Noise2Code approach is conceptually simple, computationally efficient, and generalizable to real-world degradation scenarios
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