Learning Noise-Resistant Image Representation by Aligning Clean and Noisy Domains

Recent supervised and unsupervised image representation learning algorithms have achieved quantum leaps. However, these techniques do not account for representation resilience against noise in their design paradigms. Consequently, these effective methods suffer failure when confronted with noise outside the training distribution, such as complicated real-world noise that is usually opaque to model training. To address this issue, dual domains are optimized to separately model a canonical space for noisy representations, namely the Noise-Robust (NR) domain, and a twinned canonical clean space, namely the Noise-Free (NF) domain, by maximizing the interaction information between the representations. Given the dual canonical domains, we design a target-guided implicit neural mapping function to accurately translate the NR representations to the NF domain, yielding noise-resistant representations by eliminating noise regencies. The proposed method is a scalable module that can be readily integrated into existing learning systems to improve their robustness against noise. Comprehensive trials of various tasks using both synthetic and real-world noisy data demonstrate that the proposed Target-Guided Dual-Domain Translation (TDDT) method is able to achieve remarkable performance and robustness in the face of complex noisy images.

Semantic-Aware Latent Space Exploration for Face Image Restoration

For image restoration, most existing deep learning based methods tend to overfit the training data leading to bad results when encountering unseen degradations out of the assumptions for training. To improve the robustness, generative adversarial network (GAN) prior based methods have been proposed, revealing a promising capability to restore photo-realistic and high-quality results. But these methods suffer from semantic confusion, especially on semantically significant images such as face images. In this paper, we propose a semantic-aware latent space exploration method for image restoration (SAIR). By explicitly modeling referenced semantics information, SAIR can consistently restore severely degraded images not only to high-resolution highly-realistic looks but also to correct semantics. Quantitative and qualitative experiments collectively demonstrate the effectiveness of the proposed SAIR. Our code can be found in https://github.com/Liamkuo/SAIR.

Functional Neural Networks for Parametric Image Restoration Problems

Almost every single image restoration problem has a closely related parameter, such as the scale factor in super-resolution, the noise level in image denoising, and the quality factor in JPEG deblocking. Although recent studies on image restoration problems have achieved great success due to the development of deep neural networks, they handle the parameter involved in an unsophisticated way. Most previous researchers either treat problems with different parameter levels as independent tasks, and train a specific model for each parameter level; or simply ignore the parameter, and train a single model for all parameter levels. The two popular approaches have their own shortcomings. The former is inefficient in computing and the latter is ineffective in performance. In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model. Unlike a plain neural network, the smallest conceptual element of our FuncNet is no longer a floating-point variable, but a function of the parameter of the problem. This feature makes it both efficient and effective for a parametric problem. We apply FuncNet to super-resolution, image denoising, and JPEG deblocking. The experimental results show the superiority of our FuncNet on all three parametric image restoration tasks over the state of the arts.