FaceShop: Deep Sketch-based Face Image Editing

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.Comment: 13 pages, 20 figure

Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.Comment: BMVC 201

DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

A Perceptually Optimized and Self-Calibrated Tone Mapping Operator

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with human judgments of tone-mapped image quality. In Stage two, the input HDR image is self-calibrated to compute the final LDR image. We feed the same HDR image but rescaled with different maximum luminances to the learned tone mapping network, and generate a pseudo-multi-exposure image stack with different detail visibility and color saturation. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of the structural similarity index for multi-exposure image fusion (MEF-SSIM), which has been proven perceptually relevant to fused image quality. The proposed self-calibration mechanism through MEF enables our TMO to accept uncalibrated HDR images, while being physiology-driven. Extensive experiments show that our method produces images with consistently better visual quality. Additionally, since our method builds upon three lightweight DNNs, it is among the fastest local TMOs.Comment: 20 pages,18 figure

Towards practical deep learning based image restoration model

Image Restoration (IR) is a task of reconstructing the latent image from its degraded observations. It has become an important research area in computer vision and image processing, and has wide applications in the imaging industry. Conventional methods apply inverse filtering or optimization-based approaches to restore images corrupted in ideal cases. The limited restoration performance on ill-posed problems and the low-efficient iterative optimization processes prevents such algorithms from being deployed to more complicated industry applications. Recently, the advanced deep Convolutional Neural Networks (CNNs) begin to model the image restoration as learning and inferring the posterior probability in a regression model, and successfully achieved remarkable performance. However, due to the data-driven nature, the models trained with simple synthetic paired data (e.g, bicubic interpolation or Gaussian noises) cannot be well adapted to more complicated inputs from real data domains. Besides, acquiring real paired data for training such models is also very challenging. In this dissertation, we discuss the data manipulation and model adaptability of the deep learning based image restoration tasks. Specifically, we study improving the model adaptability by understanding the domain difference between its training data and its expected testing data. We argue that the cause of image degradation can be various due to multiple imaging and transmission pipelines. Though complicated to analyze, for some specific imaging problems, we can still improve the performance of deep restoration models on unseen testing data by resolving the data domain differences implied in the image acquisition and formation pipeline. Our analysis focuses on digital image denoising, image restoration from more complicated degradation types beyond denoising and multi-image inpainting. For all tasks, the proposed training or adaptation strategies, based on the physical principle of the degradation formation or based on geometric assumption of the image, achieve a reasonable improvement on the restoration performance. For image denoising, we discuss the influence of the Bayer pattern of the Camera Filter Array (CFA) and the image demosaicing process on the adaptability of the deep denoising models. Specifically, for the task of denoising RAW sensor observations, we find that unifying and augmenting the data Bayer pattern during training and testing is an efficient strategy to make the well-trained denoising model Bayer-invariant. Additionally, for the RGB image denoising, demosaicing the noisy RAW images with Bayer patterns will result in the spatial-correlation of pixel noises. Therefore, we propose the pixel-shuffle down-sampling approach to break down this spatial correlation, and make the Gaussian-trained denoiser more adaptive to real RGB noisy images. Beyond denoising, we explain a more complicated degradation process involving diffraction when there are some occlusions on the imaging lens. One example is a novel imaging model called Under-Display Camera (UDC). From the perspective of optical analysis, we study the physics-based imaging processing method by deriving the forward model of the degradation, and synthesize the paired data for both conventional and deep denoising pipeline. Experiments demonstrate the effectiveness of the forward model and the deep restoration model trained with synthetic data achieves visually similar performance to the one trained with real paired images. Last, we further discuss reference-based image inpainting to restore the missing regions in the target image by reusing contents from the source image. Due to the color and spatial misalignment between the two images, we first initialize the warping by using multi-homography registration, and then propose a content-preserving Color and Spatial Transformer (CST) to refine the misalignment and color difference. We designed the CST to be scale-robust, so it mitigates the warping problems when the model is applied to testing images with different resolution. We synthesize realistic data while training the CST, and it suggests the inpainting pipeline achieves a more robust restoration performance with the proposed CST

MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation

Visible-light cameras can capture subtle physiological biomarkers without physical contact with the subject. We present the Multi-Site Physiological Monitoring (MSPM) dataset, which is the first dataset collected to support the study of simultaneous camera-based vital signs estimation from multiple locations on the body. MSPM enables research on remote photoplethysmography (rPPG), respiration rate, and pulse transit time (PTT); it contains ground-truth measurements of pulse oximetry (at multiple body locations) and blood pressure using contacting sensors. We provide thorough experiments demonstrating the suitability of MSPM to support research on rPPG, respiration rate, and PTT. Cross-dataset rPPG experiments reveal that MSPM is a challenging yet high quality dataset, with intra-dataset pulse rate mean absolute error (MAE) below 4 beats per minute (BPM), and cross-dataset pulse rate MAE below 2 BPM in certain cases. Respiration experiments find a MAE of 1.09 breaths per minute by extracting motion features from the chest. PTT experiments find that across the pairs of different body sites, there is high correlation between remote PTT and contact-measured PTT, which facilitates the possibility for future camera-based PTT research