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During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of R2 when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs.

Face super-resolution involves generating a high-resolution facial image from a low-resolution one. It is, however, quite a difficult task when the resolution difference between input and output images is too large. In order to tackle this challenge, many approaches use generative adversarial networks that are pre-trained on a large facial image dataset, but they often generate fake details and distort the person’s original face, leading to a loss of identity. Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively exploiting the same person’s high-resolution image as a reference image. First, we remove unnecessary detail information, such as hair and background, from the reference image, which may be different from the low-resolution input. Next, we align the high-resolution reference image to the low-resolution input image and blend them to generate a synthesized image. Finally, we refine the synthesized image to generate a faithful super-resolved image containing both details and identity information. Experimental results demonstrate that the proposed RPF algorithm outperforms recent state-of-the-art methods in terms of detail restoration and identity preservation, with improvements of 0.0098 and 0.0478 in LPIPS and ISC, respectively, on the CelebA-HQ dataset.

Contrast enhancement is required in many applications. Many studies have been conducted to perform contrast enhancement automatically, but most of them do not consider various personal preferences for contrast. We propose an edge-aware interactive contrast enhancement algorithm to enable a user to adjust image contrast easily according to his or her preference. A user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, the proposed algorithm generates an edge-aware mask by propagating the scribbles to nearby regions and restores an enhanced image through a neural network, called e-IceNet. The user can provide annotations iteratively until he or she obtains a desired image. We train e-IceNet on guidance images to yield reliable results for diverse input images. We also propose two differentiable losses to train e-IceNet effectively and reliably. Extensive experiments demonstrate that the proposed e-IceNet is capable of allowing users to enhance images satisfactorily with simple scribbles, as well as producing enhanced images automatically.

Face super-resolution is a domain-specific super-resolution task to generate a high-resolution facial image from a low-resolution one. In this paper, we propose a novel face super-resolution network, called CollageNet, to super-resolve an input image by exploiting a reference image of an identical person at the patch level. First, we extract feature pyramids from input and reference images to exploit multi-scale information hierarchically. Next, we compute the patch-wise similarities between input and reference feature pyramids and select the $K$ most similar reference patches to each input patch. Then, we compose a collaged feature pyramid by gluing those selected patches together. Finally, we obtain a super-resolved image by blending the collaged feature pyramid and the input feature. Experimental results demonstrate that the proposed CollageNet yields state-of-the-art performances.

A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this paper, which enables a user to adjust image contrast easily according to his or her preference. Specifically, a user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, given these annotations, IceNet estimates a gamma map for the pixel-wise gamma correction. Finally, through color restoration, an enhanced image is obtained. The user may provide annotations iteratively to obtain a satisfactory image. IceNet is also capable of producing a personalized enhanced image automatically, which can serve as a basis for further adjustment if so desired. Moreover, to train IceNet effectively and reliably, we propose three differentiable losses. Extensive experiments demonstrate that IceNet can provide users with satisfactorily enhanced images.