30 research outputs found
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inverse
problems. However, most existing approaches are restricted to linear inverse
problems, limiting their applicability to more general cases. In this paper, we
build upon Denoising Diffusion Restoration Models (DDRM) and propose a method
for solving some non-linear inverse problems. We leverage the pseudo-inverse
operator used in DDRM and generalize this concept for other measurement
operators, which allows us to use pre-trained unconditional diffusion models
for applications such as JPEG artifact correction. We empirically demonstrate
the effectiveness of our approach across various quality factors, attaining
performance levels that are on par with state-of-the-art methods trained
specifically for the JPEG restoration task.Comment: Presented at NeurIPS 2022 Workshop on Score-Based Methods. Code:
https://github.com/bahjat-kawar/ddrm-jpe
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds
Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. The best currently available denoising methods
approximate this mapping with cleverly engineered algorithms. In this work we
attempt to learn this mapping directly with plain multi layer perceptrons (MLP)
applied to image patches. We will show that by training on large image
databases we are able to outperform the current state-of-the-art image
denoising methods. In addition, our method achieves results that are superior
to one type of theoretical bound and goes a large way toward closing the gap
with a second type of theoretical bound. Our approach is easily adapted to less
extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG
artifacts, salt-and-pepper noise and noise resembling stripes, for which we
achieve excellent results as well. We will show that combining a block-matching
procedure with MLPs can further improve the results on certain images. In a
second paper, we detail the training trade-offs and the inner mechanisms of our
MLPs
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
We propose an image restoration algorithm that can control the perceptual
quality and/or the mean square error (MSE) of any pre-trained model, trading
one over the other at test time. Our algorithm is few-shot: Given about a dozen
images restored by the model, it can significantly improve the perceptual
quality and/or the MSE of the model for newly restored images without further
training. Our approach is motivated by a recent theoretical result that links
between the minimum MSE (MMSE) predictor and the predictor that minimizes the
MSE under a perfect perceptual quality constraint. Specifically, it has been
shown that the latter can be obtained by optimally transporting the output of
the former, such that its distribution matches the source data. Thus, to
improve the perceptual quality of a predictor that was originally trained to
minimize MSE, we approximate the optimal transport by a linear transformation
in the latent space of a variational auto-encoder, which we compute in
closed-form using empirical means and covariances. Going beyond the theory, we
find that applying the same procedure on models that were initially trained to
achieve high perceptual quality, typically improves their perceptual quality
even further. And by interpolating the results with the original output of the
model, we can improve their MSE on the expense of perceptual quality. We
illustrate our method on a variety of degradations applied to general content
images of arbitrary dimensions