Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging.

High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot.

Learning-based denoising for polarimetric images.

Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising.

Learning to Reconstruct and Understand Indoor Scenes From Sparse Views.

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation for indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g.

High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering.

Capturing images at high ISO modes will introduce much realistic noise, which is difficult to be removed by traditional denoising methods. In this paper, we propose a novel denoising method for high ISO JPEG images via deep fusion of collaborative and convolutional filtering. Collaborative filtering explores the non-local similarity of natural images, while convolutional filtering takes advantage of the large capacity of convolutional neural networks (CNNs) to infer noise from noisy images.

Depth Super-resolution from RGB-D Pairs with Transform and Spatial Domain Regularization.

This paper proposes a depth super-resolution method with both transform and spatial domain regularization. In the transform domain regularization, nonlocal correlations are exploited via an auto-regressive model, where each patch is further sparsified with a locally-trained transform to consider intra-patch correlations. In the spatial domain regularization, we propose a multi-directional total variation (MTV) prior to characterize the geometrical structures spatially orientated at arbitrary directions in depth maps.

Contrast Enhancement Based on Intrinsic Image Decomposition.

In this paper, we propose to introduce intrinsic image decomposition priors into decomposition models for contrast enhancement. Since image decomposition is a highly illposed problem, we introduce constraints on both reflectance and illumination layers to yield a highly reliable solution. We regularize the reflectance layer to be piecewise constant by introducing a weighted ℓ norm constraint on neighboring pixels according to the color similarity, so that the decomposed reflectance would not be affected much by the illumination information.

Textured Image Demoiréing via Signal Decomposition and Guided Filtering.

Moiré artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering.

Depth Map Super-Resolution Considering View Synthesis Quality.

Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account.

Resonance Character of Copper/Silver/Gold Bonding in Small Molecule⋅⋅⋅M-X (X=F, Cl, Br, CH3, CF3) Complexes.

The resonance character of Cu/Ag/Au bonding is investigated in B⋅⋅⋅M-X (M=Cu, Ag, Au; X=F, Cl, Br, CH3, CF3; B=CO, H2O, H2S, C2H2, C2H4) complexes. The natural bond orbital/natural resonance theory results strongly support the general resonance-type three-center/four-electron (3c/4e) picture of Cu/Ag/Au bonding, B:M-X↔B(+) -M:X(-) , which mainly arises from hyperconjugation interactions. On the basis of such resonance-type bonding mechanisms, the ligand effects in the more strongly bound OC⋅⋅⋅M-X series are analyzed, and distinct competition between CO and the axial ligand X is observed.

Ligand effects due to resonance character in LAuCCH(-) (L = F, Cl, Br, I, CCH) complexes: an NBO/NRT analysis.

The organogold complexes of LAuCCH(-) (L = F, Cl, Br, I, CCH) were investigated using natural bond orbital/natural resonance theory (NBO/NRT) methods. The NBO/NRT results strongly support the general resonance-type three-center-four-electron (3c/4e) picture of LAuCCH: L(-): Au-CCH ↔ L-Au :CCH(-), arising from hyperconjugation interactions. The sums of ionic and covalent contributions to both L-Au and Au-CCH bonds are all slightly larger than that due to the additional π-back bonding within the 3c/4e hyperbonded triad.

Image denoising by exploring external and internal correlations.

Single image denoising suffers from limited data collection within a noisy image. In this paper, we propose a novel image denoising scheme, which explores both internal and external correlations with the help of web images. For each noisy patch, we build internal and external data cubes by finding similar patches from the noisy and web images, respectively.

Landmark image super-resolution by retrieving web images.

This paper proposes a new super-resolution (SR) scheme for landmark images by retrieving correlated web images. Using correlated web images significantly improves the exemplar-based SR. Given a low-resolution (LR) image, we extract local descriptors from its up-sampled version and bundle the descriptors according to their spatial relationship to retrieve correlated high-resolution (HR) images from the web.