201results about How to "Efficient reconstruction" patented technology

This invention concerns a network comprising a master and slaves. In the network, each slave figures out its performance values as performance parameters and transmits the performance parameters to the master. The master performs a process of selecting a backup master as a candidate for the next master, on the basis of the performance parameters transmitted from the slaves. The master supplies BM information containing synchronization information and the address of the backup master selected, to the slaves. When the master is removed from the network, the slaves determine the next master in accordance with the backup-master information. Thus, the slaves can be immediately connected to the next master.

A headphone down mix signal can be efficiently derived from a parametric down mix of a multi-channel signal, when modified HRTFs (head related transfer functions) are derived from HRTFs of a multi-channel signal using a level parameter having information on a level relation between two channels of the multi-channel signals such that a modified HRTF is stronger influenced by the HRTF of a channel having a higher level than by the HRTF of a channel having a lower level. Modified HRTFs are derived within the decoding process taking into account the relative strength of the channels associated to the HRTFs. The HRTFs are thus modified such that a down mix signal of a parametric representation of a multi-channel signal can directly be used to synthesize the headphone down mix signal without the need of an intermediate full parametric multi-channel reconstruction of the parametric down mix.

The present invention is based on the finding that a reconstructed output channel, reconstructed with a multi-channel reconstructor using at least one downmix channel derived by downmixing a plurality of original channels and using a parameter representation including additional information on a temporal fine structure of an original channel can be reconstructed efficiently with high quality, when a generator for generating a direct signal component and a diffuse signal component based on the downmix channel is used. The quality can be essentially enhanced, if only the direct signal component is modified such that the temporal fine structure of the reconstructed output channel is fitting a desired temporal fine structure, indicated by the additional information on the temporal fine structure transmitted.

A method of compressed sensing imaging includes acquiring a sparse digital image b, said image comprising a plurality of intensities corresponding to an I-dimensional grid of points, initializing points (x^(k), y^(k)), wherein x^(k) is an element of a first expanded image x defined by b=RΦ⁻¹x, wherein R is a Fourier transform matrix, Φ is a wavelet transform matrix, y^(k) is a point in

∇_i is a forward finite difference operator for a i^th coordinate, and k is an iteration counter; calculating a first auxiliary variable s^(k) from

wherein τ₁,α are predetermined positive scalar constants, the sum is over all points n in x, and L* is an adjoint of operator L=(∇₁, . . . , ∇_I); calculating a second auxiliary variable t_n^(k) from y_n^(k)+τ₂L_nΦ⁻¹x^(k), wherein τ₂ is a predetermined positive scalar constant; updating x^(k+1) from sign(s^(k))max{0,|s^(k)|−τ₁β}, wherein β is a predetermined positive scalar constant; and updating y_n^(k+1) from

The invention discloses a video super-resolution reconstruction method based on a deep residual network. According to the method, a corresponding high-resolution image is reconstructed from a set of continuous low-resolution video frame images of a video sequence so that the video display effect can be obviously enhanced. The innovativeness of the video super-resolution algorithm is mainly reflected in two aspects: firstly, the initial stage, the series convolutional layer computation stage and the residual block computation stage are directly performed from the low-resolution video images by using the deep residual network and then the high-resolution video image is reconstructed by using the deconvolution and convolution operation mode gradually so that conventional preprocessing of bicubic interpolation does not need to be performed on the low-resolution video images; and secondly, compared with the most classic single frame and video super-resolution reconstruction method based on deep learning, the high-resolution video image can be effectively reconstructed in different environments under the condition of using few training data, and the video image display effect can be greatly enhanced.

Embodiments of the present invention disclose a technique for providing an indication whether data stored on a disk drive are invalid. As used herein, invalid data are data written prior to the disk drive being added to an array of the disk drives or data in a block that has become free and which has been removed from the corresponding parity block of the stripe. Knowing that the disk drive was written prior to the drive being added to the existing array or having data which has become invalid allows a storage server to ignore the invalid data and not to use it when computing parity (i.e., a data protection value computed as a result of a logical operation on data blocks in a stripe in the array of disk drives). This, in turn, eliminates the need to zero disk drives or to perform parity re-computation prior to using the disk drives.

A method for deriving an image sensor's 3D pose estimate from a 2D scene image input includes at least one Machine Learning algorithm trained a priori to generate a 3D depth map estimate from the 2D image input, which is used in conjunction with physical attributes of the source imaging device to make an accurate estimate of the imaging device 3D location and orientation relative to the 3D content of the imaged scene. The system may optionally employ additional Machine Learning algorithms to recognize objects within the scene to further infer contextual information about the scene, such as the image sensor pose estimate relative to the floor plane or the gravity vector. The resultant refined imaging device localization data can be applied to static (picture) or dynamic (video), 2D or 3D images, and is useful in many applications, most specifically for the purposes of improving the realism and accuracy of primarily static, but also dynamic Augmented Reality (AR) applications.

A storage system architecture comprises one or more volumes distributed across the plurality of nodes interconnected as a cluster. The volumes are organized as a striped volume set (SVS) and configured to store content of data containers served by the cluster in response to data access requests issued by clients. The content of each data container is apportioned among the volumes of the SVS to thereby improve efficiency and storage service provided by the cluster. Each data container is implemented on each of the volumes of the SVS as a sparse data container which stores data amongst sections of sparseness within the data container.

The invention discloses a super-resolution imaging system based on a compression coding aperture and an imaging method thereof, mainly solving a problem of expensive imaging cost in the prior art. The method comprises the following steps: designing a convolution template, and making a coding aperture according to coherence of a light source; placing the prepared coding aperture at a position of aperture diaphragm in an optical system and pressing a shutter for imaging, and obtaining a low resolution coding image; transmitting the coding image to a master control computer, decoding super-resolution to reconstruct a high-resolution image, and using a denoising algorithm to remove an artificial trace in the high-resolution image. The system and the method are characterized in that: restriction of a Nyquist criterion is broken through, low frequency sampling is carried out on a scene, the high-resolution image is obtained through super-resolution reconstruction, data waste caused by first sampling and second compression of a traditional imaging system is overcome, in sampling, data volume is compressed, imaging cost, compression cost and transmission cost are reduced, and the system and the method can be used for infrared imaging and remote sensing imaging technology.

Embodiments of the present invention provide systems, devices and methods for managing skew within a polarized multi-channel optical transport system. In a DP-QPSK system, skew between polarized channels is compensated within the transport system by adding latency to at least one of the polarized channels. The amount of added latency may depend on various factors including the skew tolerance of the transport system and the amount of skew across the channels without compensation. This latency may be added optically or electrically, and at various locations on a channel signal path within a transport node, such as a terminal transmitter or receiver. Additionally, various embodiments of the invention provide for novel methods of inserting frame alignment bit sequences within the transport frame overhead so that alignment and skew compensation may be more efficiently and accurately performed at the transport receiver.

The invention relates to a non local joint sparse representation based hyperspectral image super-resolution reconstruction method. The method comprises: firstly, performing dictionary training on a low-spatial-resolution hyperspectral image with an online dictionary training method to obtain a corresponding spectral dictionary; secondly, performing joint sparse representation on similar pixel vectors by virtue of a full-color image of a same scene, and reconstructing a high-resolution image; and finally, processing the reconstructed high-resolution image by utilizing an iterative reverse projection technology to obtain a high-resolution hyperspectral image with smaller reconstruction error and higher visual quality. According to the method, the similar pixel vectors are subjected to non local joint sparse representation by utilizing the non local self-similarity property of the image, so that the visual quality of the reconstructed image is improved and structural features of edges, textures and the like of the image are reconstructed more effectively in an empty region while image spectral information is kept complete; and multiple wavebands of the hyperspectral image are subjected to sparse representation and reconstruction at the same time, so that the hyperspectral image with relatively high definition and identification degree can be reconstructed.

A method of processing geological log data to construct missing information from destroyed or occluded parts using cues from observed data comprises the steps of: a. in respect of one or more data dimensions associated with missing values in a log data set, decomposing the signal into a plurality of morphological components; and b. morphologically reconstructing the signal such that missing values are estimated.