Truong Nguyen

Joint Defogging and Demosaicking

Image defogging is a technique used extensively for enhancing visual quality of images in bad weather conditions. Even though defogging algorithms have been well studied, defogging performance is degraded by demosaicking artifacts and sensor noise amplification in distant scenes. In order to improve the visual quality of restored images, we propose a novel approach to perform defogging and demosaicking simultaneously. We conclude that better defogging performance with fewer artifacts can be achieved when a defogging algorithm is combined with a demosaicking algorithm simultaneously. We also demonstrate that the proposed joint algorithm has the benefit of suppressing noise amplification in distant scenes. In addition, we validate our theoretical analysis and observations for both synthesized data sets with ground truth fog-free images and natural scene data sets captured in a raw format.

Multimodal sparse Bayesian dictionary learning applied to multimodal data classification

In this paper, we present a novel multimodal sparse dictionary learning algorithm based on a hierarchical sparse Bayesian framework. The framework allows for enforcing joint sparsity across dictionaries without restricting the actual entries to be equal. We show that the proposed method is able to learn dictionaries of higher quality than existing approaches. We validate our claims with extensive experiments on synthetic data as well as real-world data.

External short circuit fault diagnosis based on supervised statistical learning

This paper proposes a fault diagnosis method for external short circuit detection based on the supervised statistical learning. The maximum likelihood estimator is used to capture the statistical properties of the fault and non-fault datasets, and the Gaussian classifier is applied to distinguish the two states. Validation experiments demonstrate the good performance of the proposed method in dynamic conditions. Compared to the prevailing fault detection methods, this method does not require extensive modeling work, determines the fault completely based on the data in existing fault occurrence, and can be adopted easily with the trend of big data and connected vehicles.

Fast External Denoising Using Pre-Learned Transformations

We introduce a new external denoising algorithm that utilizes pre-learned transformations to accelerate filter calculations during runtime. The proposed fast external denoising (FED) algorithm shares characteristics of the powerful Targeted Image Denoising (TID) and Expected Patch Log-Likelihood (EPLL) algorithms. By moving computationally demanding steps to an offline learning stage, the proposed approach aims to find a balance between processing speed and obtaining high quality denoising estimates. We evaluate FED on three datasets with targeted databases (text, face and license plates) and also on a set of generic images without a targeted database. We show that, like TID, the proposed approach is extremely effective when the transformations are learned using a targeted database. We also demonstrate that FED converges to competitive solutions faster than EPLL and is orders of magnitude faster than TID while providing comparable denoising performance.

Low-complexity object detection with deep convolutional neural network for embedded systems

We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 − 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.