Soltanayev, Shakarim

Department of Electrical EngineeringImage denoising is a one of the most important tasks in computer vision that can improve the performance of more higher level tasks such as image classification, segmentation and image restoration by providing cleaner images. Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D.  They are typically trained to minimize the mean squared error (MSE) between the output image of a deep neural network (DNN) and a ground truth image. Thus, it is important for deep-learning-based denoisers to use high quality noiseless ground truth data for high performance.

However, it is often challenging or even infeasible to obtain noiseless images in some applications such as medical imaging and hyperspectral remote sensing and, where it is expensive or even infeasible to acquire noiseless ground truth data. For example, in hyperspectral imaging there can be substantial noise in imaging sensors due to hundreds of spectral information per pixel. Long acquisitions may improve image quality, but it is challenging to perform them with spaceborne or airborne imaging. Similarly, in X-ray CT, it is possible to obtain cleaner images by increasing the radiation dose, however this can lead to harmful effects in scanned subjects.

In this work, we propose a method based on Stein's unbiased risk estimator (SURE) for training DNN denoisers based only on the use of noisy images in the training data with Gaussian noise. We demonstrate that our SURE-based method, without the use of ground truth data, is able to train DNN denoisers to yield performances close to those networks trained with ground truth for both grayscale and color images. Our method also supports training of blind image denoisers which do not require the knowledge of the Gaussian noise standard deviation and thus can denoise images contaminated with wide variety of noise levels. We also propose a SURE-based refining method with a noisy test image for further performance improvement.  Our quick refining method outperformed conventional BM3D, deep image prior, and often the networks trained with ground truth. Potential extension of our SURE-based methods to Poisson noise model was also investigated to demonstrate the ability of our method to generalize to more noise distributions.clos

Training and refining deep learning based denoisers without ground truth data

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