Abstract
Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.
Highlights
Image denoising [1] is an image processing task with a long history, and has a wide range of application scenarios because noise contamination is inevitable in any image sensing and transmission process
Researchers believed that images are generally sparse in the gradient domain and transform domain, and proposed the well-known total variation (TV) regularizer [6] and transform domain sparsity [7]; they immediately found that these regularizers could not describe the local features of images well
Wiener filtering is utilized to carry out denoising again for the purpose of obtaining the final estimation. Besides these two classic methods, nonlocal image denoising methods include the low-rank approach [13], which discovers the matrices grouped by 2D image blocks that have the low-rank property; the nonlocal model is based on a weighted nuclear norm constraint and its varieties [14,15], and is an extension of the low-rank approach but assigns different weights to the coefficients; and the Bayesian modeling method, i.e., the simultaneous sparse coding with Gaussian scale mixture (SSC-GSM) method [16]
Summary
Image denoising [1] is an image processing task with a long history, and has a wide range of application scenarios because noise contamination is inevitable in any image sensing and transmission process. In contrast to model-based methods that enforce a solution to obey some well-designed prior distributions based on statistics, learning-based methods directly learn mapping functions or sparse transform bases to estimate the missing high-frequency details from the observed noisy image or a large number of external samples. They can be divided into two categories by either learning the sparse representation or learning the deep networks. Thanks to the process of iterative optimization and nonlocal regularization, our method has better adaptability and flexibility, and gains better performance than the original DIP method
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