Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.

There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate high quality colorized images of a particular character with only a few training data. Seg2pix is designed to reproduce a segmented image, which becomes the suggestion data for line art colorization. The segmented image is automatically generated through a generative network with a line art image and a segmentation ground truth. In the next step, this generative network creates a colorized image from the line art and segmented image, which is generated from the former step of the generative network. To summarize, only one line art image is required for testing the generative model, and an original colorized image and segmented image are additionally required as the ground truth for training the model. These generations of the segmented image and colorized image proceed by an end-to-end method sharing the same loss functions. By using this method, we produce better qualitative results for automatic colorization of a particular character’s line art. This improvement can also be measured by quantitative results with Learned Perceptual Image Patch Similarity (LPIPS) comparison. We believe this may help artists exercise their creative expertise mainly in the area where computerization is not yet capable.

Background/Objectives: Ptosis surgery corrects drooping upper eyelids, improving function and esthetics. Traditional methods like marginal reflex distance (MRD) and palpebral fissure height (PFH) offer limited one-dimensional measurements. This study evaluates AI-based corneal exposure ratio (CER) analysis, a two-dimensional approach, compared to manual ImageJ methods for assessing ptosis surgery outcomes. Methods: In this prospective study, 100 eyes from 50 patients were analyzed using both methods. AI-based CER measurements were compared to manual ImageJ measurements for reliability and accuracy. Results: AI-based CER measurements were comparable to manual ImageJ, with high reliability (ICC 0.992, 0.985). Preoperative CER was 55.34% (manual) and 55.79% (AI), increasing to 75.92% (manual) and 75.84% (AI) postoperatively. The AI tool showed minimal bias and high repeatability (ICC 1.000), offering faster automated measurements. Conclusions: AI-based CER analysis matched manual methods in accuracy but provided significant efficiency advantages, making it suitable for clinical use. Limitations include a small homogeneous sample size and reliance on 2D imaging, which may not fully capture three-dimensional changes. Further studies are recommended to enhance generalizability and precision.

Traditional perceptual loss functions, primarily designed for photographic images, often perform poorly in the sketch domain due to significant differences in visual representation. To address this domain gap, we propose an improved perceptual loss specifically designed for sketch images. Our method fine-tunes a pre-trained VGG-16 model on the ImageNet-Sketch dataset while strategically replacing max-pooling layers with spatial and channel attention mechanisms. We comprehensively evaluate our approach across three dimensions: generation quality, sketch retrieval performance, and feature space organization. Experimental results demonstrate consistent improvements across all evaluation metrics, with our method achieving the best generation performance, over 10% improvement in sketch retrieval accuracy, and 6-fold improvement in class separability compared to baseline methods. The ablation studies confirm that both fine-tuning and attention mechanisms are essential components that work together effectively. Our domain-specific perceptual loss effectively bridges the gap between photographic and sketch domains, providing enhanced performance for various sketch-related computer vision applications, including generation, retrieval, and recognition.

Oculoplastic surgery is a critical treatment for various eye conditions, such as ptosis, which can cause both aesthetic and functional issues. Due to the anxiety about the outcome, patients are often hesitant to undergo the necessary procedures required for the surgery. Virtual oculoplastic surgery simulation technology offers a solution to alleviate these concerns by providing realistic previews of post-surgical results. In this paper, we present a novel deep learning-based virtual oculoplastic surgery simulation system that addresses the limitations of existing methods. The proposed system aims to improve the accuracy of simulations by considering the anatomical structure and characteristics of the eye. Our method utilizes a deformable parametric mesh to enhance the controllability of the image transformation process. Furthermore, the combination of a style-based generator and a neural texture has been implemented to generate high-quality results. The proposed system is expected to facilitate better communication between doctors and patients by providing anatomically inspired high-quality simulation results. The development of this advanced virtual simulation system has the potential to enhance patient experiences and improve satisfaction with outcomes in the field of oculoplastic surgery.