Query: Image denoising

title: Image denoising using deep CNN with batch renormalization
abstract: Deep convolutional neural networks (CNNs) have attracted great attention in the field of image denoising. However, there are two drawbacks: (1) it is very difficult to train a deeper CNN for denoising tasks, and (2) most of deeper CNNs suffer from performance saturation. In this paper, we report the design of a novel network called a batch renormalization denoising network (BRDNet) Specifically, we combine two networks to increase the width of the network, and thus obtain more features. Because batch renormalization is fused into BRDNet, we can address the internal covariate shift and small mini batch problems. Residual learning is also adopted in a holistic way to facilitate the network training. Dilated convolutions are exploited to extract more information for denoising tasks. Extensive experimental results show that BRDNet outperforms state of the art image denoising methods. The code of BRDNet is accessible at http:/www.yongxu.org/lunwen.html.

title: Attention guided CNN for image denoising
abstract: Deep convolutional neural networks (CNNs) have attracted considerable interest in low level computer vision. Researches are usually devoted to improving the performance via very deep CNNs. However, as the depth increases, influences of the shallow layers on deep layers are weakened. Inspired by the fact, we propose an attention guided denoising convolutional neural network (ADNet) mainly including a sparse block (SB) a feature enhancement block (FEB) an attention block (AB) and a reconstruction block (RB) for image denoising. Specifically, the SB makes a tradeoff between performance and efficiency by using dilated and common convolutions to remove the noise. The FEB integrates global and local features information via a long path to enhance the expressive ability of the denoising model. The AB is used to finely extract the noise information hidden in the complex background, which is very effective for complex noisy images, especially real noisy images and bind denoising. Also, the FEB is integrated with the AB to improve the efficiency and reduce the complexity for training a denoising model. Finally, a RB aims to construct the clean image through the obtained noise mapping and the given noisy image. Additionally, comprehensive experiments show that the proposed ADNet performs very well in three tasks (i.e. synthetic and real noisy images, and blind denoising) in terms of both quantitative and qualitative evaluations. The code of ADNet is accessible at https:/github.com/hellloxiaotian/ADNet.

title: Connecting Image Denoising and High Level Vision Tasks via Deep Learning
abstract: Image denoising and high level vision tasks are usually handled independently in the conventional practice of computer vision, and their connection is fragile. In this paper, we cope with the two jointly and explore the mutual influence between them with the focus on two questions, namely (1) how image denoising can help improving high level vision tasks, and (2) how the semantic information from high level vision tasks can be used to guide image denoising. First for image denoising we propose a convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales. Second we propose a deep neural network solution that cascades two modules for image denoising and various high level tasks, respectively, and use the joint loss for updating only the denoising network via back propagation. We experimentally show that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high level vision tasks. On the other hand, with the guidance of high level vision information, the denoising network produces more visually appealing results. Extensive experiments demonstrate the benefit of exploiting image semantics simultaneously for image denoising and high level vision tasks via deep learning. The code is available online: https:/github.com/Ding Liu/DeepDenoising

title: Deep Learning on Image Denoising: An overview
abstract: Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state of the art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.

title: Image denoising review: From classical to state of the art approaches
abstract: At the crossing of the statistical and functional analysis, there exists a relentless quest for an efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising algorithms have been documented in the literature, in spite of which the level of functionality of these algorithms still holds margin to acquire desired level of applicability. Quite often noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image. Based on some specific set of assumptions all methods work optimally, however they tend to create artefacts and remove fine structural details under general conditions. This article focuses on classifying and comparing some of the significant works in the field of denoising.

title: NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
abstract: This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+ This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state of the art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https:/bit.ly/siddplus_data.

title: Deep Graph Convolutional Image Denoising
abstract: Non local self similarity is well known to be an effective prior for the image denoising problem. However, little work has been done to incorporate it in convolutional neural networks, which surpass non local model based methods despite only exploiting local information. In this paper, we propose a novel end to end trainable neural network architecture employing layers based on graph convolution operations, thereby creating neurons with non local receptive fields. The graph convolution operation generalizes the classic convolution to arbitrary graphs. In this work, the graph is dynamically computed from similarities among the hidden features of the network, so that the powerful representation learning capabilities of the network are exploited to uncover self similar patterns. We introduce a lightweight Edge Conditioned Convolution which addresses vanishing gradient and over parameterization issues of this particular graph convolution. Extensive experiments show state of the art performance with improved qualitative and quantitative results on both synthetic Gaussian noise and real noise.

title: Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning
abstract: Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network's generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state of the art color image denoising performance on every single noise level, by an average of $0.1dB$ whether trained on or not.

title: Real World Image Denoising with Deep Boosting
abstract: We propose a Deep Boosting Framework (DBF) for real world image denoising by integrating the deep learning technique into the boosting algorithm. The DBF replaces conventional handcrafted boosting units by elaborate convolutional neural networks, which brings notable advantages in terms of both performance and speed. We design a lightweight Dense Dilated Fusion Network (DDFN) as an embodiment of the boosting unit, which addresses the vanishing of gradients during training due to the cascading of networks while promoting the efficiency of limited parameters. The capabilities of the proposed method are first validated on several representative simulation tasks including non blind and blind Gaussian denoising and JPEG image deblocking. We then focus on a practical scenario to tackle with the complex and challenging real world noise. To facilitate leaning based methods including ours, we build a new Real world Image Denoising (RID) dataset, which contains 200 pairs of high resolution images with diverse scene content under various shooting conditions. Moreover, we conduct comprehensive analysis on the domain shift issue for real world denoising and propose an effective one shot domain transfer scheme to address this issue. Comprehensive experiments on widely used benchmarks demonstrate that the proposed method significantly surpasses existing methods on the task of real world image denoising. Code and dataset are available at https:/github.com/ngchc/deepBoosting.

title: Memory Efficient Hierarchical Neural Architecture Search for Image Denoising
abstract: Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS) an effort towards employing NAS to automatically design effective neural network architectures for image denoising. HiNAS adopts gradient based search strategies and employs operations with adaptive receptive field to build an flexible hierarchical search space. During the search stage, HiNAS shares cells across different feature levels to save memory and employ an early stopping strategy to avoid the collapse issue in NAS, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient, which takes only about 4.5 hours for searching using a single GPU. We evaluate the effectiveness of our proposed HiNAS on two different datasets, namely an additive white Gaussian noise dataset BSD500, and a realistic noise dataset SIM1800. Experimental results show that the architecture found by HiNAS has fewer parameters and enjoys a faster inference speed, while achieving highly competitive performance compared with state of the art methods. We also present analysis on the architectures found by NAS. HiNAS also shows good performance on experiments for image de raining.