High-Resolution Network for Photorealistic Style Transfer (1904.11617v1)

Abstract: Photorealistic style transfer aims to transfer the style of one image to another, but preserves the original structure and detail outline of the content image, which makes the content image still look like a real shot after the style transfer. Although some realistic image styling methods have been proposed, these methods are vulnerable to lose the details of the content image and produce some irregular distortion structures. In this paper, we use a high-resolution network as the image generation network. Compared to other methods, which reduce the resolution and then restore the high resolution, our generation network maintains high resolution throughout the process. By connecting high-resolution subnets to low-resolution subnets in parallel and repeatedly multi-scale fusion, high-resolution subnets can continuously receive information from low-resolution subnets. This allows our network to discard less information contained in the image, so the generated images may have a more elaborate structure and less distortion, which is crucial to the visual quality. We conducted extensive experiments and compared the results with existing methods. The experimental results show that our model is effective and produces better results than existing methods for photorealistic image stylization. Our source code with PyTorch framework will be publicly available at https://github.com/limingcv/Photorealistic-Style-Transfer

• The paper introduces a high-resolution network that maintains full image resolution to preserve structural and semantic details.
• It employs a parallel multi-scale architecture that fuses high- and low-resolution subnets to eliminate artifacts and reduce information loss.
• Experimental results and user studies demonstrate superior visual fidelity and efficiency compared to traditional style transfer methods.

High-Resolution Network for Photorealistic Style Transfer: An Expert Analysis

The paper "High-Resolution Network for Photorealistic Style Transfer" by Ming Li, Chunyang Ye, and Wei Li, presents an advancement in photorealistic style transfer using a high-resolution network. This research addresses the limitations of existing methods which often compromise image detail and structure integrity during style transfer. Using a high-resolution network, the authors propose maintaining resolution throughout the process, thereby reducing distortion and enhancing content fidelity.

Photorealistic style transfer is distinguished from its artistic counterpart by its objective to retain the original structural details of the input image while applying the desired style, ensuring that the output still resembles a realistic photograph. The challenge in this domain is to avoid semantic degradation and structural distortions, which commonly occur in conventional and neural style transfer algorithms, as indicated by results in Figure 1 of the paper.

Methodology

The authors introduce a high-resolution generation network designed to handle photorealistic stylization without reducing the image resolution partway through the process. The network architecture innovatively connects high-resolution subnets with low-resolution subnets in parallel, facilitating continuous multi-scale fusion. This approach contrasts with traditional networks that often downsample and subsequently upsample images, leading to information loss and artifacts.

They leverage a VGG19 network for computing content and style loss, instead of the more commonly used VGG16, identifying the former as more effective for their purposes. The perceptual loss functions aim at achieving a balance between maintaining photorealism and effectively transferring style, with specific architectural choices like the bottleneck residual design used to optimize training efficiency and visual output quality.

Experimental Results

The paper provides comprehensive experiments contrasting their method against several established photorealistic and artistic style transfer algorithms, including those by Gatys et al. (2016) and Reinhard et al. (2001). The evaluation criteria comprised computational efficiency, output quality in terms of semantic retention, and user preference studies.

Quantitative results indicate that the proposed model not only reduces computational expense but also outpaces competitors in maintaining fine image detail, as visually assessed in Figures 5 and 6. User studies corroborate these findings, highlighting preferences for the outputs generated by the high-resolution network employed by the authors, particularly in semantic adherence and visual realism.

Implications and Future Directions

The implications of this work extend practically and theoretically. The practical aspect encompasses enhanced efficiency in photorealistic processing which is vital for applications requiring high fidelity image manipulation. Theoretically, the work advances understanding in multi-resolution architectures, suggesting the high-resolution network scheme as a potent alternative in visual tasks demanding high fidelity outputs.

Future research could focus on real-time style transfer by refining this approach or evaluating the high-resolution characteristics for broader applications like video processing or real-time surveillance. Additionally, incorporating instance-aware processing could provide targeted style application, enhancing customization beyond the current capabilities.

In summary, this paper contributes significantly to the field of photorealistic style transfer by successfully implementing a high-resolution network that mitigates common pitfalls of resolution-dependent distortions and semantic loss. This approach has set a commendable precedent for future explorations in this vibrant area of computer vision.