154 research outputs found
Complex-valued Retrievals From Noisy Images Using Diffusion Models
In diverse microscopy modalities, sensors measure only real-valued
intensities. Additionally, the sensor readouts are affected by
Poissonian-distributed photon noise. Traditional restoration algorithms
typically aim to minimize the mean squared error (MSE) between the original and
recovered images. This often leads to blurry outcomes with poor perceptual
quality. Recently, deep diffusion models (DDMs) have proven to be highly
capable of sampling images from the a-posteriori probability of the sought
variables, resulting in visually pleasing high-quality images. These models
have mostly been suggested for real-valued images suffering from Gaussian
noise. In this study, we generalize annealed Langevin Dynamics, a type of DDM,
to tackle the fundamental challenges in optical imaging of complex-valued
objects (and real images) affected by Poisson noise. We apply our algorithm to
various optical scenarios, such as Fourier Ptychography, Phase Retrieval, and
Poisson denoising. Our algorithm is evaluated on simulations and biological
empirical data.Comment: 11 pages, 7figure
Blind Image Denoising using Supervised and Unsupervised Learning
Image denoising is an important problem in image processing and computer vision. In real-world applications, denoising is often a pre-processing step (so-called low-level vision task) before image segmentation, object detection, and recognition at higher levels. Traditional image denoising algorithms often make idealistic assumptions with the noise (e.g., additive white Gaussian or Poisson). However, the noise in the real-world images such as high-ISO photos and microscopic fluorescence images are more complex. Accordingly, the performance of those traditional approaches degrades rapidly on real-world data. Such blind image denoising has remained an open problem in the literature.
In this project, we report two competing approaches toward blind image denoising: supervised and unsupervised learning. We report the principles, performance, differences, merits, and technical potential of a few blind denoising algorithms.
Supervised learning is a regression model like CNN with a large number of pairs of corrupted images and clean images. This feed-forward convolution neural network separates noise from the image. The reason for using CNN is its deep architecture for exploiting image characteristics, possible parallel computation with modern powerful GPU’s and advances in regularization and learning methods to train. The integration of residual learning and batch normalization is effective in speeding up the training and improving the denoising performance. Here we apply basic statistical reasoning to signaling reconstruction to map corrupted observations to clean targets
Recently, few deep learning algorithms have been investigated that do not require ground truth training images. Noise2Noise is an unsupervised training method created for various applications including denoising with Gaussian, Poisson noise. In the N2N model, we observe that we can often learn to turn bad images to good images just by looking at bad images. An experimental study is conducted on practical properties of noisy-target training at performance levels close to using the clean target data. Further, Noise2Void(N2V) is a self-supervised method that takes one step further. This is method does not require clean image data nor noisy image data for training. It is directly trained on the current image that is to be denoised where other methods cannot do it. This is useful for datasets where we cannot find either a noisy dataset or a pair of clean images for training i.e., biomedical image data
Image Denoising using Attention-Residual Convolutional Neural Networks
During the image acquisition process, noise is usually added to the data
mainly due to physical limitations of the acquisition sensor, and also
regarding imprecisions during the data transmission and manipulation. In that
sense, the resultant image needs to be processed to attenuate its noise without
losing details. Non-learning-based strategies such as filter-based and noise
prior modeling have been adopted to solve the image denoising problem.
Nowadays, learning-based denoising techniques showed to be much more effective
and flexible approaches, such as Residual Convolutional Neural Networks. Here,
we propose a new learning-based non-blind denoising technique named Attention
Residual Convolutional Neural Network (ARCNN), and its extension to blind
denoising named Flexible Attention Residual Convolutional Neural Network
(FARCNN). The proposed methods try to learn the underlying noise expectation
using an Attention-Residual mechanism. Experiments on public datasets corrupted
by different levels of Gaussian and Poisson noise support the effectiveness of
the proposed approaches against some state-of-the-art image denoising methods.
ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for
Gaussian and Poisson denoising, respectively FARCNN presented very consistent
results, even with slightly worsen performance compared to ARCNN.Comment: Published in: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and
Images (SIBGRAPI
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