Deep Networks for Compressed Image Sensing

The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained superior performance. However, there still exist two important challenges within the CS theory. The first one is how to design a sampling mechanism to achieve an optimal sampling efficiency, and the second one is how to perform the reconstruction to get the highest quality to achieve an optimal signal recovery. In this paper, we try to deal with these two problems with a deep network. First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process. Then, we propose a deep network to recover the image, which imitates traditional compressed sensing reconstruction processes. Experimental results demonstrate that our deep networks based CS reconstruction method offers a very significant quality improvement compared against state of the art ones.Comment: This paper has been accepted by the IEEE International Conference on Multimedia and Expo (ICME) 201

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.Comment: ICCV 201

Precise Tide-independent Bathymetric Survey and Application to the Inshore Monitoring of Seabed Evolution

Due to the influences of tidal water level, vessel attitude and wave motion, the traditional bathymetric method of reducing depths by the tidal level makes it difficult to meet precise engineering requirements in the vertical direction. Therefore a precise method, termed a tide-independent bathymetric survey, is presented in this paper. In this method, the quality of the sounding and positioning solution, the influences of time offset and vessel attitude as well as height transformation are considered by taking a series of measurements. The tide-independent method has been used for inshore monitoring of the seabed sediments. The statistical parameters acquired by comparing the traditional method with the tide-independent method used in the monitoring show that the latter is accurate and credible.Debido a las influencias del nivel del agua de las mareas, la actitud de los buques y el movimiento de las alas, el metoda batimetrico tradicional de reduccion de profundidades mediante los niveles de las mareas dificulta el cumplimiento de los requerimientos precisos de ingenieroa en la direccion vertical. Asf pues, en este articulo se presenta un metoda preciso, denominado levantamiento batimetrico independiente de las mareas. En este metoda, la calidad de los sondeos y la solucion del posicionamiento, las influencias del desfase horario y de la actitud de la nave, asl como la transformacion de las altura son consideradas mediante la toma de una serie de mediciones. El metoda independiente de las mareas ha sido utilizado para el control costero de los sedimentos del fonda del mar. Los parametres estadisticos adquiridos comparando el metoda tradicional con el metoda independiente de las mareas utilizado en el control muestran que este ultimo es exacto y creible.En raison de l'influence du niveau de la maree, de l'attitude du navire et du mouvement de l'eau, la methode bathymetrique traditionnelle de reduction des profondeurs par le niveau de l'eau rend difficile la satisfaction d'exigences precises d'ingenierie dans la direction verticale. Cet article presente done une methode precise, appelee leve bathymetrique independant des marees. Dans cette methode, la qualite du systeme de sondages et de determination de la position, les influences des decalages temporels et l'attitude du navire ainsi que la transformation de la hauteur sont pris en compte dans une serie de mesures. La methode independante des marees a ete utilisee pour le controle cotier des sediments du fond de la mer. Les parametres statistiques obtenus en comparant la methode traditionnelle a la methode independante des marees utilisee montre que cette derniere est exacte et credible

Light Field Saliency Detection with Deep Convolutional Networks

Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light field image benefits from the additional modeling of angular patterns. For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection. However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs. In addition, current light field datasets are not sufficiently large to train CNNs. To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps. Compared to current light field saliency datasets [1], [2], our new dataset is larger, of higher quality, contains more variation and more types of light field inputs. This makes our dataset suitable for training deeper networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images. We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency. Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets.Comment: 14 pages, 14 figure

Mobile E-business Platform: Collaboration System of Wi-Fi and WAVE Based on Cognitive Radio

The new international standard IEEE802.11p (WAVE) defined enhancements to IEEE802.11 (Wi-Fi). A new mobile e-business platform of Wi-Fi and WAVE is described. Based on cognitive radio, the popular wireless network Wi-Fi can collaborate with WAVE in their common unlicensed spectrum band and licensed spectrum band. As a core technology in the cognitive radio field, OFDM is introduced, and a novel frequency estimation algorithm for them is presented