Perceptual Adversarial Networks for Image-to-Image Transformation (1706.09138v2)

Abstract: In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks. Among them, the hidden layers and output of the discriminative network D are upgraded to continually and automatically discover the discrepancy between the transformed image and the corresponding ground-truth. Simultaneously, the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through the adversarial training process, the image transformation network T will continually narrow the gap between transformed images and ground-truth images. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining, image inpainting, etc.) show that the proposed PAN outperforms many related state-of-the-art methods.

• The paper demonstrates a novel approach that integrates traditional adversarial loss with a dynamic perceptual adversarial loss to enhance image quality.
• It employs a dual-network design where the transformation network maps inputs to targets and the discriminator adapts via high-level perceptual features.
• Experimental results across tasks like image de-raining, semantic segmentation, and inpainting show improved metrics such as SSIM and VIF compared to standard methods.

Perceptual Adversarial Networks for Image-to-Image Transformation

The paper "Perceptual Adversarial Networks for Image-to-Image Transformation" presents an innovative approach to tackling image-to-image transformation tasks using a framework termed Perceptual Adversarial Networks (PAN). This work is a significant contribution to the field of image processing, employing deep learning methodologies to enhance generated image quality across various applications such as image de-raining, semantic segmentation, and image inpainting.

Core Concepts and Methodology

The core innovation of the paper lies in integrating two types of adversarial losses: the traditional generative adversarial loss from GANs and a novel perceptual adversarial loss. The proposed framework consists of two major components: the image transformation network $T$ and the discriminative network $D$. The transformation network $T$ learns to map input images to target images, aiming to minimize perceptual discrepancies, while the discriminative network $D$ distinguishes generated images from real ones.

One of the groundbreaking ideas in PAN is the introduction of the perceptual adversarial loss. Unlike conventional methods that rely on static pre-trained models for perceptual feature extraction, PAN dynamically updates hidden layers in the discriminator to measure discrepancies in high-level perceptual features between transformed images and their ground-truth. This approach allows for continuous adaptation and improvement in capturing perceptual differences, enabling more realistic image generation.

Experimental Evaluation

The paper provides extensive experimental evaluations on a set of well-known image-to-image transformation tasks:

• Image De-raining: PAN demonstrates enhanced capabilities in removing rain streaks without introducing artifacts, achieving superior results in both qualitative and quantitative metrics compared to existing methodologies.
• Semantic Labels to Image Generation: Through tasks such as translating semantic labels to cityscapes images, PAN achieves better detail preservation and higher-quality outputs. Quantitative comparisons indicate improvements in metrics like SSIM, UQI, and VIF.
• Image Inpainting: By filling in missing parts of images, PAN's perceptual adversarial loss shows a noticeable improvement in preserving semantic coherency and reducing perceptual discrepancy compared to the Context-Encoder baseline.

Implications and Future Work

The implications of this work are substantial for practical applications in computer vision and graphics. The introduction of perceptual adversarial loss provides a powerful tool for unsupervised image generation tasks, where understanding high-level semantics is crucial. Additionally, the technique holds promise for extensions into other areas such as video prediction and domain adaptation tasks.

Furthermore, the authors hint at utilizing PAN for unpaired image translations, showing potential for enhancing unpaired GAN frameworks like CycleGAN by introducing perceptual consistency across domains. This could further benefit tasks where paired training data are unavailable.

The PAN framework successfully bridges the gap between adversarial and perceptual learning strategies, demonstrating robust capabilities in image-to-image transformations. Future developments might explore more sophisticated architectures and integration with other high-dimensional features to further elevate performance. The adaptive nature of the perceptual adversarial loss opens numerous avenues for research, particularly in refining adversarial training processes and applications beyond image synthesis tasks.

In summary, this paper provides a comprehensive and evaluated approach to improve image transformation tasks using adversarial and perceptual strategies, contributing valuable insights and techniques to the domain of neural network-based image processing.