Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Tensor Decomposition Based Beamspace ESPRIT for Millimeter Wave MIMO Channel Estimation

Abstract: We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes.

3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network

Abstract: The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

Mistake resistance image transmission method based on Polar code technology

Abstract: The invention discloses a mistake resistance image transmission method based on a Polar code technology, comprising steps of performing polarized operation on a channel to enable the channel condition to polarize, performing assessment on the reliability of the polarized channel and sorting the assessment results, reading images, performing DCT transformation on the image to obtain frequency components, sorting the frequency components according to the information entropy, performing one-to-one correspondence on the frequency components and the channel condition, using important low frequency components as information bits and using high frequency components as frozen bits to send into the channel for transmission after encoding according to the Polar encoding rule. The mistake resistance image transmission method based on a Polar code technology is good in resisting mistakes and high in transmission efficiency. Under the same compression ratio, compared with the direct masking compression, the transmission method disclosed by the invention has better mistake resistance, and the PSNR of the restored image is much improved by comparing with the PSNR of the direct compression image.

Video processing method, device and system

Abstract: The invention discloses a video processing method, which is used for transmitting multi-channel video content. The method comprises the following steps: acquiring multi-channel video data to be synthesized; judging whether the resolution of the video data of each channel to be synthesized exceeds a resolution threshold; if so, adjusting the resolution to be equal to or lower than the resolution threshold; and merging the multi-channel video data to be synthesized into one-channel video data. The invention also discloses a device and a system for realizing the method.

High-DR frame-free PWM imaging with asynchronous AER intensity encoding and focal-plane temporal redundancy suppression

Abstract: The presented asynchronous, time-based CMOS dynamic vision and image sensor is based on a QVGA (304×240) array of fully autonomous pixels containing event-based change detection and PWM imaging circuitry. Exposure measurements are initiated and carried out locally by the individual pixel that has detected a brightness change in its field-of-view. Thus pixels do not rely on external timing signals and independently and asynchronously request access to an (asynchronous arbitrated) output channel when they have new illumination values to communicate. Communication is address-event based (AER)-gray-levels are encoded in inter-event intervals. Pixels that are not stimulated visually do not produce output. This pixel-autonomous and massively parallel operation ideally results in optimal lossless video compression through complete temporal redundancy suppression at the focal-plane. Compression factors depend on scene activity. Due to the time-based encoding of the illumination information, very high dynamic range — intra-scene DR of 143dB static and 125dB at 30fps equivalent temporal resolution — is achieved. A novel time-domain correlated double sampling (TCDS) method yields array FPN of 56dB (9.3bit) for >10Lx.