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Multi-Scale Face Restoration With Sequential Gating Ensemble Network

AAAI Conferences

Restoring face images from distortions is important in face recognition applications and is challenged by multiple scale issues, which is still not well-solved in research area. In this paper, we present a Sequential Gating Ensemble Network (SGEN) for multi-scale face restoration issue. We first employ the principle of ensemble learning into SGEN architecture design to reinforce predictive performance of the network. The SGEN aggregates multi-level base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive field. Instead of combining these base-en/decoders directly with non-sequential operations, the SGEN takes base-en/decoders from different levels as sequential data. Specifically, the SGEN learns to sequentially extract high level information from base-encoders in bottom-up manner and restore low level information from base-decoders in top-down manner. Besides, we propose to realize bottom-up and top-down information combination and selection with Sequential Gating Unit (SGU). The SGU sequentially takes two inputs from different levels and decides the output based on one active input. Experiment results demonstrate that our SGEN is more effective at multi-scale human face restoration with more image details and less noise than state-of-the-art image restoration models. By using adversarial training, SGEN also produces more visually preferred results than other models through subjective evaluation.


A new way to train AI systems could keep them safer from hackers

MIT Technology Review

The context: One of the greatest unsolved flaws of deep learning is its vulnerability to so-called adversarial attacks. When added to the input of an AI system, these perturbations, seemingly random or undetectable to the human eye, can make things go completely awry. Stickers strategically placed on a stop sign, for example, can trick a self-driving car into seeing a speed limit sign for 45 miles per hour, while stickers on a road can confuse a Tesla into veering into the wrong lane. Safety critical: Most adversarial research focuses on image recognition systems, but deep-learning-based image reconstruction systems are vulnerable too. This is particularly troubling in health care, where the latter are often used to reconstruct medical images like CT or MRI scans from x-ray data.


Enhancing your photos through artificial intelligence

#artificialintelligence

This post has been republished via RSS; it originally appeared at: Microsoft Research. The amount of visual data we accumulate around the world is mind boggling. However, not all the images are captured by high-end DSLR cameras, and very often they suffer from imperfections. It is of tremendous benefit to save those degraded images so that users can reuse them for their own design or other aesthetic purposes. In this blog, we are going to present our latest efforts in image enhancement.


Photo finish: Two new AI methods for improving quality of photographs

#artificialintelligence

The amount of visual data we accumulate around the world is mind boggling. However, not all the images are captured by high-end DSLR cameras, and very often they suffer from imperfections. It is of tremendous benefit to save those degraded images so that users can reuse them for their own design or other aesthetic purposes. In this blog, we are going to present our latest efforts in image enhancement. The first technique enhances the image resolution of an image file by referring to external reference images.


Multi-defect microscopy image restoration under limited data conditions

arXiv.org Machine Learning

Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging. One of the major challenges in application of such methods is the availability of training data. In this work, we pro-pose a unified method for reconstruction of multi-defect fluorescence microscopy images when training data is limited. Our approach consists of two steps: first, we perform data augmentation using Generative Adversarial Network (GAN) with conditional instance normalization (CIN); second, we train a conditional GAN(cGAN) on paired ground-truth and defected images to perform restoration. The experiments on three common types of imaging defects with different amounts of training data, show that the proposed method gives comparable results or outperforms CARE, deblurGAN and CycleGAN in restored image quality when limited data is available.