Deep Learning on Image Denoising: An overview <s> Deep learning techniques for blind denoising <s> Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images to speed up the inference, and adopts orthogonal regularization to enhance the generalization ability. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map, and (iii) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications. <s> BIB001 </s> Deep Learning on Image Denoising: An overview <s> Deep learning techniques for blind denoising <s> To estimate the levels of signal-dependent noise (SDN) from a single image is challenging. This paper proposes a novel method to estimate the noise level function (NLF) from a single image using a Multi-column Convolutional Neural Network (MC-Net) with an end-to-end architecture. The MC-Net is trained on a synthesized dataset containing noisy images with known NLFs, and it allows to learn rich hierarchical features using three sub-networks. Moreover, this method performs end-to-end training to retain more details for pixel-wise noise level estimation. Experimental results indicate that our method is accurate and robust to estimate NLFs of SDN for various types of images. <s> BIB002 </s> Deep Learning on Image Denoising: An overview <s> Deep learning techniques for blind denoising <s> The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs. <s> BIB003 </s> Deep Learning on Image Denoising: An overview <s> Deep learning techniques for blind denoising <s> The term blind denoising refers to the fact that the basis used for denoising is learnt from the noisy sample itself during denoising. Dictionary learning and transform learning based formulations for blind denoising are well known. But there has been no autoencoder based solution for the said blind denoising approach. So far autoencoder based denoising formulations have learnt the model on a separate training data and have used the learnt model to denoise test samples. Such a methodology fails when the test image (to denoise) is not of the same kind as the models learnt with. This will be first work, where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method performs better than dictionary learning (KSVD), transform learning, sparse stacked denoising autoencoder and the gold standard BM3D algorithm. <s> BIB004 </s> Deep Learning on Image Denoising: An overview <s> Deep learning techniques for blind denoising <s> The removal of mixed noise is a stiff problem since the distribution of the noise cannot be predicted accurately. The most common mixed noise is the combination of Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). Many methods first attempt to remove IN but it might collapse the texture of the image. In this paper, we propose a new learning-based method using convolutional neural network (CNN) for removing mixed gaussian-impulse noise. Since our denoising network can remove various level of mixed noise, neither the preprocessing for removing IN nor noise-level estimation is necessary. <s> BIB005
In the real world, the image is easily corrupted and noise is complex. Thus, blind denoising technique is important  . At first, FFDNet BIB001 used noise level and noise as the input of CNN to train a denoiser for unknown noisy image. Then, scholars proposed a lot of methods to solve blind denoisng problem. According to the mechanism of image device, Kenzo et al. BIB003 utilized soft shrinkage to adjust the noise level for blind denoising. For unpaired noisy image, using CNNs to estimate noise became a good tool  . Yang et al. BIB002 used known noise level to train a denoiser, then, they utilized this denoiser to estimate the level of noise. For random noise attenuation problem, CNN with RL was used to filter complex noise   . Additionally, changing network architecture can promote the denoising performance for blind denoising. Majumdar et al. BIB004 presented to use auto-encoder to tackle unknown noise. For mixed noise, cascaded CNNs were effective to remove the AWAG and impulse noise, respectively BIB005 . To clear show these denoising methods, Table 6 is designed as follows. BIB002 CNN Blind denoising GAN for blind image denoising It is known that noise is ruleless and complex in the real world. Thus, blind denoising techniques, especially deep learning techniques are developed. For this reason, comparing the denoising performance of different deep learning techniques is very meaningful. The state-of-the-art denoising methods such as DnCNN, FFDNet, SCNN and G2G1 on the BSD68 and Set12 are chosen to design experiments. As shown in Tables 16 and 17 , the FFDNet is superior to other methods in blind denoising.