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JDSR-GAN: Constructing A Joint and Collaborative Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some comparable methods which perform the previous two tasks separately.


Introduction
Recently, most people are suffering from the outbreak of novel coronavirus 2019 (COVID- 19). The world health organization (WHO) has pointed out that wearing a mask is an effective way to prevent the spread of the COVID-19 virus. With the improvement awareness of epidemic prevention, face images captured in conventional unlimited scenes such as video surveillance possess complex variations such as mask and low-resolution (LR) simultaneously. Obtaining high-resolution (HR) face images without the mask is now an essential yet challenging task, which plays an import role in many face-related security applications, e.g., face alignment [1], face parsing [2], face detection [3], face tracking [4], and face recognition [5,6]. Although many existing approaches have achieved promising progress in attaining high-quality HR face samples from the related low-quality LR ones, most of them can only be used to handle one type of variation, such as LR face super-resolution or masked face image completion. In practice application scenarios (e.g., video surveillance), these approaches may not be applicable to the case where both LR and masked face are attained simultaneously.
One alternative way to deal with masked face super-resolution problem is to perform image denoising followed by a face super-resolution procedure. However, it is not known whether the denoising methods are feasible for the LR face images. Meanwhile, the efficiency of existing face super-resolution solutions is not explicit when they are used to super-resolve LR face images with a mask. As shown in Fig. 1, when a denoising algorithm (CBDNet [7]) and a face super-resolution algorithm (FSRNet [8]) are utilized in sequence to an observed masked LR face image, the super-resolved face images ( Fig. 1 (b)) may miss some facial details to a certain extent. This straightforward recovering scheme maybe not optimal because it performs denoising and super-resolution separately, which may ignore the joint and collaborative properties of these two tasks during the recovery procedure.
Different from existing solutions, our target is to tackle a more challenging problem of how to super-resolve high-quality face images from both LR and masked face inputs in a single model. To this end, in this work, we design an end-to-end joint and collaborative framework via a generative adversarial network (GAN). Through the generator, we can  [7]) and face super-resolution (FSRNet [8]) successively and by our proposed JDSR-GAN, (d) is the referenced high-resolution face images.
perform face image denoising and super-resolution simultaneously to obtain high-quality HR face images without mask from input masked low-quality face image. To obtain more accurate and fine-grained visual results, the discriminator contains seven losses:pixel loss, identity loss, smooth loss, perceptual loss, style loss, face prior loss and adversarial loss (please refer Section 3.2 for more details). We evaluate the effectiveness of our proposed approach on the available public CelebA [9] dataset. In summary, the main contributions of this work can be concluded in three-fold: (i) We introduce identity loss and attention mechanism into existing popular denoising and super-resolution methods to obtain better performance; (ii) We devise a feasible and efficient framework for jointly and collaboratively performing denoising and face super-resolution via a single model; (iii) We obtain promising masked face super-resolution results compared with some comparable face super-resolution approaches especially for the low-quality face images obtained from real-world scenes.

Related work
In this part, we simply review some related typical image denoising and image superresolution approaches for better understanding of our proposed method.

Image Denoising
In recent years, on account of the remarkable achievement of deep neural networks in image classification, image denoising approaches based on deep learning have been developed.
Xie et al. [10] employed stacked denoising auto-encoder for image denoising and image inpainting, whose framework is a multi-layer fully connected network. Zhang et al. [11] combined residual learning [12] and batch normalization [13] to propose a denoising model (DnCNN) addressing the gradient dispersion caused by deepening of the network layers.
Furthermore, the noise in practical images is derived from various scenes. Blind denoising of practical noisy images is still a challenging task. Gong et al. [14] modeled the data fitting term as a weighted sum of L 1 and L 2 norms and utilized a sparse regularizer in the wavelet domain to deal with mixed or unknown noise. Zhu et al. [15] proposed to model image noise using a mixed Gaussian (MoG) model and developed a low-rank MoG filter to recover clean images. Zhang et al. [7] proposed a CBDNet composed of a noise estimation subnet and a non-blind denoising subnet, where the asymmetric loss was introduced to suppress underestimation errors of noise levels. Anwar et al. [16] utilized a residual on the residual structures to facilitate low-frequency features, and applied future attention to exploit the correlation of channels. In addition to noise simulation of RGB images, Brooks et al. [17] analyzed the image signal processing channel and then generated raw images directly by inverting each step of an image processing pipeline. Tian et al. [18] exploited a dual CNN with residual learning, dilated convolutions, and batch renormalization to tackle the real noisy image. Wang et al. [19] proposed a novel k-Sigma transform that allows the model to remove the ISO constraint, enabling the small network to efficiently tackle an extensive range of noise levels.

Image Super-Resolution
The target of the single image super-resolution (SR) is to recover HR images from corresponding LR inputs. In recent years, deep neural networks have been broadly adopted for the super-resolution task. Ledig et al. [20] presented a generative adversarial network based method for photo-realistic images super-resolution utilizing a perceptual loss function. Tai et al. [21] presented a deep end-to-end durable memory framework to solve the long-term dependency issue of image recovery. Huang et al. [22] turned to the domain of wavelet and proposed a network for predicting wavelet coefficients of HR images. Zhang et al. [23] presented to adaptively reassign channel-wise features by a channel attention mechanism, considering the interdependence between channels. Li et al. [24] designed an image superresolution feedback network (SRFBN) to achieve a better SR performance. Guo et al. [25] proposed a dual regression network (DRN) by introducing an additional dual regression mapping on LR data. Face image super-resolution is a class-specific image recognition method that exploits the statistical properties of face images [26]. Earlier techniques assumed that faces are in a controlled environment with tiny variations. Kanade and Baker [27] presented a prior learning method for the spatial distribution of the face image gradient for frontal images. Yang et al. [28] used mappings between specific facial components to incorporate the face priors. Yu et al. [29] introduced the deep discriminative generative network to recover very low-quality face images. Moreover, Yu et al. [30] embedded attributes in the procedure of face image super-resolution. Chen et al. [8] and Song et al. [31] both used a multi-task approach for coarse-to-fine face super-resolution. Then, Zhang et al. [32] introduced a super identity loss to evaluate the differences of identity information. Cai et al. [33] proposed a framework to alternately optimize two complementary tasks, namely face image super-resolution and completion by multi-task learning. Hu et al. [34] propsoed a definition-scalable inference method to super-resolve HR faces from real low-quality faces.
Hsu et al. [35] leveraged the facial identity information for identity-preserving face SR task.
Recently, Ma et al. [36] propose a face SR method with iterative collaboration between facial image recovery and landmark estimation.

Proposed Method
Inspired by the former denoising and super-resolution methods, we design a multi-task training strategy for network learning, aiming to remove the masks and perform face superresolution simultaneously. Fig. 4 depict the whole pipeline of our proposed method, which is composed of a generator, a discriminator, and the related losses. The denoising module is shown in Fig. 2, and the face super-resolution module is given in Fig. 3. We now explain each part in detail.

Network Architecture
Denoising Module: CBDNet [7] has achieved good performance at removing Gaussian noise but has not been studied for removing the mask in face images. The channel attention mechanism can be utilized to filter out the important points from a mass of information and enhance the capabilities of the network to identify different contributions of the feature maps. Based on the CBDNet, we add channel attention to each convolution block in the network to construct our denoising module. As illustrated in Fig. 2, the denoising network can be decomposed into a noise evaluation subnetwork CN N E and a non-blind denoising subnetwork CN N D , aiming to generate an LR non-masked image I D LR from an input masked LR face image I M LR . The LR face image without mask addressed by the denoising module can be represented as Figure 3: Network structure of the face super-resolution module. "Conv" embedded in the main stream depicts a convolutional layer together with the Batch Normalization [13] and ReLU [37] operations. "Strided Conv" indicates the convolutional layer with the size of the kernel be 3×3 and the stride to be 2.
where [·], and γ denote the procedure of concatenation and estimated noise level map respectively.
Face Super-Resolution Module: After the denoising module, the face image I D LR is fed into the following super-resolution module to get a high-quality face image without the mask. Similar to the previous operations, we introduce channel attention into each residual block in FSRNet [8] as show in Fig. 3. The face super-resolution module is composed of a coarse-SR network, a prior estimation network, an encoder, and a decoder network, which takes the geometry prior, i.e., face parsing maps and facial landmark heatmaps into consideration. The process of face super-resolution can be formulated as where I C , I P , I M ix ,and I SR represents the coarse SR image recovered from I D LR , prior estimation evaluated from I C , the concatenation of image feature and prior estimation, and the final output high-resolution non-masked face image respectively.
Generator and Discriminator: Images generated by conventional super-resolution methods lack high-frequency information and fine details, which can only be remedied by selecting the appropriate target functions. While GAN can solve this problem, it has exhibited great potential in super-resolution, generating photo-realistic images with superior visual effects [20]. As depicted in Fig. 4, the generator of our JDSR-GAN consists of an image denoising module and successively a super-resolution module. Ideally, given an observed low-quality masked face image I M LR , the output face image by the generator should be a non-masked face image with high resolution.
We use a discriminator network to distinguish the real HR images and the super-resolved ones, which plays an auxiliary character in our network training. The structure of our discriminator is the same as that in WGAN-GP [38]. WGAN-GP removes weight clipping from WGAN [39] and adds the gradient penalty to discriminator loss, enabling the networks to converge fast and stably. The loss function of our discriminator is given as where p r denotes the distribution of the real face images, p g denotes the distribution of the SR face images and px can be defined as the data distribution sampled from p r and p g . ∇x denotes the gradient operator. η denotes the penalty coefficient, which is set as 0.1 in this paper.
In our experiments, extensive evaluations have proven that our proposed approach is feasible and effective. Our multi-task training strategies take advantage of the complementary information of the two tasks so that we can obtain fine-grained face recovery images with fewer artifacts. Moreover, we also need to carefully design appropriate loss functions for the entire network. We will detail these in the next part.

Loss Functions
Asymmetric loss and total variation (TV) regularization. The non-blind denoising model is very sensitive to noise level, so we introduce asymmetrical loss into the noise estimation subnetwork to avoid estimation error of noise level. When the estimated noise level is lower than the real level, more penalties will be added. The asymmetric loss can be defined as where β (γ(y i )−γ(y i )) represents a mathematical expression when β = 1 forγ(y i ) − γ(y i ) < 0 and 0 otherwise,γ(y i ), γ(y i ) represent the estimated noise level and corresponding ground truth at pixel i respectively, y represents the synthetic noisy image, and α is a parameter set between 0 and 0.5.
Since many recovery algorithms amplify the noise, we incorporate a total variation regularization, which constrains the smoothness of the image pixels to ensure that the horizontal and vertical pixel changes of the image shrink to a certain range. The TV loss can be defined as where ∇ v and ∇ h represent the gradient operator along the vertical direction and horizontal direction respectively.
Pixel loss. In fact, L 2 loss posses a strong penalty for large errors and a weak penalty for small errors, neglecting the impact of the image content itself, i.e., generates smoother images. However, if texture or grid appears, then optimizing L 2 loss can easily grind this area. Furthermore, the convergence performance of L 2 loss is worse than that of L 1 loss.
Thus, the pixel loss can be defined as where || · || 1 denotes the L 1 norm, I GT LR and I GT HR denote the ground-truth non-masked LR face image and the ground-truth non-masked HR face image respectively.
Perceptual loss. Previous super-resolution methods mostly used mean square error (MSE) as loss function. Although good super-resolution results can be obtained by minimizing MSE loss, it is difficult to avoid fuzzy details, which is caused by the flaws of MSE itself. Thus, we use perceptual loss here, which will make the restored image look better in visual effect. The perceptual loss is formulated as where φ denotes VGG − 16 [40] pre-trained on ImageNet [41], φ i,j denotes the feature from the j − th convolution layer ahead of the i − th max pooling layer, W i,j and H i,j denote the size of the map mentioned above.
Smooth loss. When we conduct face image denoising, the obtained images may exhibit trivial color distortions around the boundaries of the masked area. Thus, we also incorporate the smooth loss to alleviate such distortions. Meanwhile, the smooth loss can effectively constrain the gradient to prevent gradient explosions. The formula is as follows where H and W denote the height and width of the recovered image, respectively.
Style loss. During the process of denoising, an essential task is to render the style of the denoising area that looks similar enough to the non-masked area. Thus, we incorporate the tyle loss [42] into the denoising module which works by merging the contextual content of the output image with that of the ground-truth one. To measure the difference between image styles is equivalent to compare the differences of their Gram matrixes. The style loss is defined as where F n is a normalization factor 1/(C n · W n · H n ) for the n − th VGG − 16 layer. C n , W n and H n denote the channel number, width and height of the maps, respectively.
Face prior loss. In our network, we expected that the generator can remove the mask and produce more realistic face images. However, the generated face images may show large deviations around the mouth, nose, or other components which means that essential facial geometry is missing. To tackle this problem, we also adopt the face prior as a complementary discriminator [8]. The network introduces two related face priors, face parsing, and face alignment, as the supplementary evaluation metrics, penalizing the discrepancy between the geometry of the generated images and the ground-truth ones. The named face prior loss is formulated as where H parsing p , L landmark p , H parsing gt and L landmark gt denote the estimated face parsing maps and face landmark maps from the recovered images, the referenced face parsing maps and face landmark heatmaps, respectively. Empirically, we set µ = 1 and ν = 0.1.
Identity loss. Pioneer work [32] has revealed that identity is an important criterion in terms of distinguishing each object. We expect that the super-resolved images have a similar identity as their target ones. Thus, we further introduce identity loss into the training process, aiming to enhance image fidelity and identity recognition. In this paper, we use a Resnet-like CN N [12] as the face feature extraction network (i.e., CN N E in Table 1). The identity loss can be defined as where CN N E (I SR ) and CN N E (I GT HR ) are the identity features of images I SR and I GT HR extracted by the model CN N E .
For the entire network, L de and L f sr are integrated to make the denoising module and face super-resolution complement each other. The total loss can be represented as

Dataset and Metrics
We validate the performance of respective methods on CelebA [9] face dataset. CelebA is a widely used large-scale dataset that contains 10,177 face objects and 202,599 samples.
Following the previous protocol, we use 162,770 to construct the training set, 19,867 images to construct the validation set, and 19,962 images test set. In real-world application scenes, it is unreasonable to acquire coupled face images, i.e., clean face samples and their corresponding faces with the mask. To obtain the faces with the mask, we use a face detection method [43] to detect the location of key points in each face of CelebA, and then put a nature mask on each face automatically based on the mask-wearing software developed in [44].
Some examples of masked faces are given in Fig. 5. The similarity between the ground-truth face images and recovered ones are evaluated in terms of SSIM and PSNR [45], which are  evaluated on the Y channel in the converted YCbCr space.

Implementation Details
To obtain the ground truth of face parsing maps on CelebA dataset, we utilize GFC [2] trained on the Helen [46] dataset as the face parsing instrument to estimate the parsing results. During the pre-training of the face parsing network, we explore Adam [47] method with an initial learning rate as 10 −4 . Following [33], for the ground truth of facial landmarks on CelebA, we also exploit the public available SeetaFace model to estimate the 81 landmarks for each face image. For the multi-task training, we crop and normalize the face regions in CelebA dataset to the size 128 × 128. Then we add a mask into each face image and

Ablation Study
In our method, we have several loss functions and channel attention mechanism compared with previous related methods. In this part, we perform ablation experiments to assess the effectiveness of each member. All the studies are performed based on the same subset of the large-scale CelebA dataset, using the same masked low-quality face images with scale factor 4 (i.e., the size of the input is 32 × 32). The quantitative performance of the study is tabulated in Table 2. From the result, we can observe that when the model loses the constraint provided by the style loss and perceptual loss, the quality of the SR images is degraded since its ability to measure the reconstruction difference is weakened. A large improvement can also be observed from the channel attention, smooth loss, and identity information, which enables the network to flexibly capture the relationship between global and local features. The above ablation studies prove that each part of JDSR-GAN has an indispensable contribution to the improvement of the performance.  [24], DRN [25], SICNN [32], FSRGAN [8], DICGAN [36], our JDSR-GAN and the ground-truth HR references.

Experimental Comparisons
In this part, we compare our method with some state-of-the-art ones. The compared methods include two general image SR methods (SRFBN [24] and DRN [25]) and three face image SR methods (SICNN [32], FSRGAN [8], and DICGAN [36]). It is worthy that for those prominent SR methods, we first perform face denoising process on the LR inputs by the CBDNet [7] method. For a fair comparison, the CBDNet and those successive SR methods are pre-trained based on the same training set.
We first conduct experiments to validate the performance of our JDSR-GAN in removing the mask occlusion. The results are shown in Fig. 6, from which we can see that by The qualitative comparisons of respective methods are listed in Fig. 7 and Fig. 8. Compared with other methods, the SR images generated by our proposed JDSR-GAN can obtain quite better visual effects and can recover more facial details. Although the super-resolved face image is slightly different from the ground-truth ones around the mouth, the facial detail features are generally more similar to the ground-truth references. The quantitative comparisons are also given in Table 3. By jointly performing denoising and SR task, our JDSR-GAN can attain remarkable PSNR and SSIM values than other compared methods, which further validate the superiority of our method.

Generality Study
In this part, we conduct experiments to study the generality of our JDSR-GAN. We use the model trained on CelebA to perform testing on Helen. The masked face images have a size of 32 × 32. The visual comparisons of our JDSR-GAN and DICGAN are shown in Fig. 9, from which we can observe that our method can attain more facial texture details around the masked area than the competitive one, which generates many artifacts around the mouth. Exhilaratingly, The recovered faces by our JDSR-GAN look more similar to the ground-truth one. In terms of the quantitative results, our JDSR-GAN achieves 25.5427 dB PSNR and 0.7719 MSSIM, which is 1.6 dB and 0.07 higher than that of the DICGAN method, respectively.

Results on Real World Image
In all the above experiments, the masks in the faces are artificially added. In real application conditions, it is unreasonable and difficult for us to simulate the process of For each person, from left to right are successively the input masked LR faces, the SR results of SICNN [32], FSRGAN [8], DICGAN [36], our JDSR-GAN and the "ground truth".
image degradation and wearing a mask. Thus, in this part, we perform experiments to testify the effectiveness of our method on real-world masked low-quality face images. The low-quality images are crawled from the Internet and resized to have a size of 128 × 128 as the inputs. The images of the same subject without a mask are regarded as the "ground truth". Fig. 10 shows the visual results of respective methods on several real low-quality images. Compared with other methods, our JDSR-GAN can obtain the best performance.
It removes most of the mask and to some extent looks more similar to the "ground truth".

Conclusions
For the face super-resolution task where the face images acquired in the video surveillance scenarios have the mask and low resolution, in this paper, we construct a joint and collaborative learning network (JDSR-GAN) to perform image denoising and super-resolution simultaneously in a single model. Our JDSR-GAN method uses multi-task learning to inte-grate channel attention mechanism and carefully designed losses to recover high-quality face images without masks from masked low-quality face images. Compared with the previous methods which consider image denoising and super-resolution separately, our JDSR-GAN integrates them together, thus provides complementary information to each part, obtaining pleasing restoration results on the benchmark dataset. Comprehensive experimental evaluations have exhibited that our proposed solution significantly outperforms comparable approaches in terms of qualitative and quantitative comparisons. For future work, we would also like to investigate whether other reasonable face priors can be used as guidance to better assist the face restoration.