Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.

Biharmonic diffusion curve images from boundary elements

Abstract: There is currently significant interest in freeform, curve-based authoring of graphic images. In particular, "diffusion curves" facilitate graphic image creation by allowing an image designer to specify naturalistic images by drawing curves and setting colour values along either side of those curves. Recently, extensions to diffusion curves based on the biharmonic equation have been proposed which provide smooth interpolation through specified colour values and allow image designers to specify colour gradient constraints at curves. We present a Boundary Element Method (BEM) for rendering diffusion curve images with smooth interpolation and gradient constraints, which generates a solved boundary element image representation. The diffusion curve image can be evaluated from the solved representation using a novel and efficient line-by-line approach. We also describe "curve-aware" upsampling, in which a full resolution diffusion curve image can be upsampled from a lower resolution image using formula evaluated orrections near curves. The BEM solved image representation is compact. It therefore offers advantages in scenarios where solved image representations are transmitted to devices for rendering and where PDE solving at the device is undesirable due to time or processing constraints.

Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution

Abstract: In-vitro fertilization (IVF), as the most common fertility treatment, has never reached its maximum potentials. Systematic selection of embryos with the highest implementation potentials is a necessary step toward enhancing the effectiveness of IVF. Embryonic cell numbers and their developmental rate are believed to correlate with the embryo’s implantation potentials. In this paper, we propose an automatic framework based on a deep convolutional neural network to take on the challenging task of automatic counting and centroid localization of embryonic cells (blastomeres) in microscopic human embryo images. In particular, the cell counting task is reformulated as an end-to-end regression problem that is based on a shape-aware Gaussian dot annotation to map the input image into an output density map. The proposed Cell-Net system incorporates two novel components, residual incremental Atrous pyramid, and progressive up-sampling convolution. The residual incremental Atrous pyramid enables the network to extract rich global contextual information without raising the ‘grinding’ issue. Progressive up-sampling convolution gradually reconstructs a high-resolution feature map by taking into account short- and long-range dependencies. The experimental results confirm that the proposed framework is capable of predicting the cell-stage and detecting blastomeres in embryo images of 1–8 cell by mean accuracies of 86.1% and 95.1%, respectively.

Multiresolution Fourier Descriptors for Multiresolution Shape Analysis

Abstract: Complex shapes can be effectively analyzed by multiresolution shape descriptors. Compared with wavelet descriptors that are widely used for multiresolution analysis, Fourier descriptors have better invariance properties and higher computational efficiency. We propose a novel multiresolution scheme to generate multiresolution Fourier descriptors for multiresolution analysis: downsampling expansion followed by upsampling reconstruction. Simulation shows that our multiresolution scheme outperforms both wavelet and traditional Fourier descriptors in terms of accuracy and efficiency.

Efficient employment of digital upsampling using IFFT in OFDM systems for simpler analog filtering

Abstract: Systems and methods are provided for transmitting OFDM information via IFFT up-sampling components that transmit data at a higher sampling rate than conventional systems to simplify filter requirements and mitigate leakage between symbols. In one embodiment, an NL point IFFT is performed on a zero inserted set of frequency domain symbols. In another embodiment, the NL point IFFT is further optimized by exploiting the fact that (N−1) L of the frequency domain symbols are zero. This enables an embodiment that consists of a pre-processor that multiplies the input samples by complex phase factors, followed by L point IFFTs.

Method and apparatus for encoding and/or decoding video data using adaptive prediction order for spatial and bit depth prediction

Abstract: A scalable video bitstream may have an H264/AVC compatible base layer (BL) and a scalable enhancement layer (EL), where scalability refers to color bit depth The H264/AVC scalability extension SVC provides also other types of scalability, eg spatial scalability where the number of pixels in BL and EL are different According to the invention, BL information is upsampled (TUp,BDUp) in two logical steps in adaptive order, one being texture upsampling and the other being bit depth upsampling Texture upsampling is a process that increases the number of pixels, and bit depth upsampling is a process that increases the number of values that each pixel can have, corresponding to the pixels color intensity The upsampled BL data are used to predict the collocated EL A prediction order indication is transferred so that the decoder can upsample BL information in the same manner as the encoder, wherein the upsampling refers to spatial and bit depth characteristics