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1. Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators

   https://doi.org/10.3390/jimaging9070133  

   Evangelista, Davide; Morotti, Elena; Piccolomini, Elena Loli; Nagy, James (July 2023, Journal of Imaging)

   In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions. In addition, networks do not necessarily take into account the numerical formulation of the underlying imaging problem when trained end-to-end. In this paper, we propose some strategies to improve stability without losing too much accuracy to deblur images with deep-learning-based methods. First, we suggest a very small neural architecture, which reduces the execution time for training, satisfying a green AI need, and does not extremely amplify noise in the computed image. Second, we introduce a unified framework where a pre-processing step balances the lack of stability of the following neural-network-based step. Two different pre-processors are presented. The former implements a strong parameter-free denoiser, and the latter is a variational-model-based regularized formulation of the latent imaging problem. This framework is also formally characterized by mathematical analysis. Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise. In particular, the model-based framework represents the most reliable trade-off between visual precision and robustness. 

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2. Robust Denoising and DenseNet Classification Framework for Plant Disease Detection

   Kevin Zhou and Dimah Dera (February 2024, the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications)

   Plant disease is one of many obstacles encountered in the field of agriculture. Machine learning models have been used to classify and detect diseases among plants by analyzing and extracting features from plant images. However, a common problem for many models is that they are trained on clean laboratory images and do not exemplify real conditions where noise can be present. In addition, the emergence of adversarial noise that can mislead models into wrong predictions poses a severe challenge to developing preserved models against noisy environments. In this paper, we propose an end-to-end robust plant disease detection framework that combines a DenseNet-based classification with a vigorous deep learning denoising model. We validate a variety of deep learning denoising models and adopt the Real Image Denoising network (RIDnet). The experiments have shown that the proposed denoising classification framework for plant disease detection is more robust against noisy or corrupted input images compared to a single classification model and can also successfully defend against adversarial noises in images. 

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3. Denoising diffusion weighted imaging data using convolutional neural networks

   https://doi.org/10.1371/journal.pone.0274396  

   Cheng, Hu; Vinci-Booher, Sophia; Wang, Jian; Caron, Bradley; Wen, Qiuting; Newman, Sharlene; Pestilli, Franco (September 2022, PLOS ONE)

   Yap, Pew-Thian (Ed.)

   Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor. 

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4. Adaptive Denoising via GainTuning

   Mohan, Sreyas; Vincent, Joshua L.; Manzorro, Ramon; Crozier, Peter A.; Simoncelli, Eero P.; Fernandez-Granda, Carlos (July 2021, ArXivorg)

   null (Ed.)

   Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing when applied to data that deviate from the training distribution. Recent work has shown that it is possible to train denoisers on a single noisy image. These models adapt to the features of the test image, but their performance is limited by the small amount of information used to train them. Here we propose "GainTuning", in which CNN models pre-trained on large datasets are adaptively and selectively adjusted for individual test images. To avoid overfitting, GainTuning optimizes a single multiplicative scaling parameter (the "Gain") of each channel in the convolutional layers of the CNN. We show that GainTuning improves state-of-the-art CNNs on standard image-denoising benchmarks, boosting their denoising performance on nearly every image in a held-out test set. These adaptive improvements are even more substantial for test images differing systematically from the training data, either in noise level or image type. We illustrate the potential of adaptive denoising in a scientific application, in which a CNN is trained on synthetic data, and tested on real transmission-electron-microscope images. In contrast to the existing methodology, GainTuning is able to faithfully reconstruct the structure of catalytic nanoparticles from these data at extremely low signal-to-noise ratios. 

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5. Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser

   Kadkhodaie, Zahra; Simoncelli, Eero (January 2021, Advances in neural information processing systems)

   Deep neural networks have provided state-of-the-art solutions for problems such as image denoising, which implicitly rely on a prior probability model of natural images. Two recent lines of work – Denoising Score Matching and Plug-and-Play – propose methodologies for drawing samples from this implicit prior and using it to solve inverse problems, respectively. Here, we develop a parsimonious and robust generalization of these ideas. We rely on a classic statistical result that shows the least-squares solution for removing additive Gaussian noise can be written directly in terms of the gradient of the log of the noisy signal density. We use this to derive a stochastic coarse-to-fine gradient ascent procedure for drawing high-probability samples from the implicit prior embedded within a CNN trained to perform blind denoising. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any deterministic linear inverse problem, with no additional training, thus extending the power of supervised learning for denoising to a much broader set of problems. The algorithm relies on minimal assumptions and exhibits robust convergence over a wide range of parameter choices. To demonstrate the generality of our method, we use it to obtain state-of-the-art levels of unsupervised performance for deblurring, super-resolution, and compressive sensing. 

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