18,413 research outputs found
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model
Optical Coherence Tomography (OCT) image denoising is a fundamental problem
as OCT images suffer from multiplicative speckle noise, resulting in poor
visibility of retinal layers. The traditional denoising methods consider
specific statistical properties of the noise, which are not always known.
Furthermore, recent deep learning-based denoising methods require paired noisy
and clean images, which are often difficult to obtain, especially medical
images. Noise2Noise family architectures are generally proposed to overcome
this issue by learning without noisy-clean image pairs. However, for that,
multiple noisy observations from a single image are typically needed. Also,
sometimes the experiments are demonstrated by simulating noises on clean
synthetic images, which is not a realistic scenario. This work shows how a
single real-world noisy observation of each image can be used to train a
denoising network. Along with a theoretical understanding, our algorithm is
experimentally validated using a publicly available OCT image dataset. Our
approach incorporates Anscombe transform to convert the multiplicative noise
model to additive Gaussian noise to make it suitable for OCT images. The
quantitative results show that this method can outperform several other methods
where a single noisy observation of an image is needed for denoising. The code
and implementation of this paper will be available publicly upon acceptance of
this paper.Comment: Accepted to MICCAI OMIA workshop 202
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
In this paper, we propose a novel approach to the rank minimization problem,
termed rank residual constraint (RRC) model. Different from existing low-rank
based approaches, such as the well-known nuclear norm minimization (NNM) and
the weighted nuclear norm minimization (WNNM), which estimate the underlying
low-rank matrix directly from the corrupted observations, we progressively
approximate the underlying low-rank matrix via minimizing the rank residual.
Through integrating the image nonlocal self-similarity (NSS) prior with the
proposed RRC model, we apply it to image restoration tasks, including image
denoising and image compression artifacts reduction. Towards this end, we first
obtain a good reference of the original image groups by using the image NSS
prior, and then the rank residual of the image groups between this reference
and the degraded image is minimized to achieve a better estimate to the desired
image. In this manner, both the reference and the estimated image are updated
gradually and jointly in each iteration. Based on the group-based sparse
representation model, we further provide a theoretical analysis on the
feasibility of the proposed RRC model. Experimental results demonstrate that
the proposed RRC model outperforms many state-of-the-art schemes in both the
objective and perceptual quality
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