Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression.

Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published method offers both lossless and near-lossless modes. In this paper, we propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals. We solve the joint lossy and residual compression problem in the approach of VAEs, and add autoregressive context modeling of the residuals to enhance lossless compression performance. In the near-lossless mode, we quantize the original residuals to satisfy a given ℓ∞ error bound, and propose a scalable near-lossless compression scheme that works for variable ℓ∞ bounds instead of training multiple networks. To expedite the DLPR coding, we increase the degree of algorithm parallelization by a novel design of coding context, and accelerate the entropy coding with adaptive residual interval. Experimental results demonstrate that the DLPR coding system achieves both the state-of-the-art lossless and near-lossless image compression performance with competitive coding speed.

High cost-efficient and computational gigapixel video camera based on commercial lenses and CMOS chips.

The state-of-the-art commercial telephoto lens has already provided us almost one giga space-bandwidth product. Since the single-image sensor cannot take such sampling capacity, we implement a four-parallel-boresight imaging system by using four such lenses and use 64 image sensors to complete full field of view (FOV) imaging for achieving 0.8 gigapixel over 15.6°×10.5°. Multiple sensors mosaicking can make most online computation and data transfer in parallel, and help us to realize a gigapixel video camera. Meanwhile, according to the four-parallel-boresight configuration, the flat image plane simplifies the image registration and image stitching, and allows us to keep high imaging performance in full frame following geometric and optical calibration and correction. Furthermore, considering that working distance changes do bring additional x/y offsets between sensor arrays, we propose a computation-based method and introduce an eight-axis automatic motion mechanism into the system to perform the online active displacement. Our prototype camera using 16 sensors has been validated in 50 m indoor conditions and 145 m outdoor condition experiments, respectively. The effective angular resolution under the 0.2 giga 24 Hz video output is 18 µrad, which is two times the lens instantaneous FOV. Compared with other gigapixel cameras, it is superior in terms of optical system simplicity and cost efficiency, which would potentially benefit numerous unmanned aerial vehicle photogrammetric applications that pursue high angular resolution over moderate FOV.

Graph-Based Blind Image Deblurring From a Single Photograph.

Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given an estimated blur kernel, de-convolve the blurry input to restore the target image. In this paper, we propose a graph-based blind image deblurring algorithm by interpreting an image patch as a signal on a weighted graph. Specifically, we first argue that a skeleton image-a proxy that retains the strong gradients of the target but smooths out the details-can be used to accurately estimate the blur kernel and has a unique bi-modal edge weight distribution. Then, we design a reweighted graph total variation (RGTV) prior that can efficiently promote a bi-modal edge weight distribution given a blurry patch. Further, to analyze RGTV in the graph frequency domain, we introduce a new weight function to represent RGTV as a graph l1-Laplacian regularizer. This leads to a graph spectral filtering interpretation of the prior with desirable properties, including robustness to noise and blur, strong piecewise smooth (PWS) filtering and sharpness promotion. Minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem. Leveraging the new graph spectral interpretation for RGTV, we design an efficient algorithm that solves for the skeleton image and the blur kernel alternately. Specifically for Gaussian blur, we propose a further speedup strategy for blind Gaussian deblurring using accelerated graph spectral filtering. Finally, with the computed blur kernel, recent non-blind image deblurring algorithms can be applied to restore the target image. Experimental results demonstrate that our algorithm successfully restores latent sharp images and outperforms state-of-the-art methods quantitatively and qualitatively.

3,5-Dicaffeoylquinic acid isolated from Artemisia argyi and its ester derivatives exert anti-leucyl-tRNA synthetase of Giardia lamblia (GlLeuRS) and potential anti-giardial effects.

An aqueous ethanol extract of Artemisia argyi inhibited the aminoacylation activity of LeuRS from Giardia lamblia (GlLeuRS). The bioassay-guided fractionation of the extract led to the isolation of 3,5-dicaffeoylquinic acid (1), with an IC50 of 5.82 µg/mL. The ester derivatives of 1 were also found to possess strong anti-GlLeuRS effects, with IC50 values of 1.79, 5.51 and 2.56 µg/mL respectively. Anti-giardial assay showed that the derivatives, especially 3,5-dicaffeoylquinic acid propyl ester (4) (IC50=4.62 µg/mL), were effective at killing G. lamblia.