Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation.

Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assumptions of optical flow. Typically, existing methods mainly adopt domain adaptation to transfer motion knowledge from clean to degraded domain through one-stage adaptation. However, this direct adaptation is ineffective, since there exists a large gap due to adverse weather and scene style between clean and real degraded domains.

I2C: Invertible Continuous Codec for High-Fidelity Variable-Rate Image Compression.

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed.

Video-rate 3D imaging of living cells using Fourier view-channel-depth light field microscopy.

Interrogation of subcellular biological dynamics occurring in a living cell often requires noninvasive imaging of the fragile cell with high spatiotemporal resolution across all three dimensions. It thereby poses big challenges to modern fluorescence microscopy implementations because the limited photon budget in a live-cell imaging task makes the achievable performance of conventional microscopy approaches compromise between their spatial resolution, volumetric imaging speed, and phototoxicity. Here, we incorporate a two-stage view-channel-depth (VCD) deep-learning reconstruction strategy with a Fourier light-field microscope based on diffractive optical element to realize fast 3D super-resolution reconstructions of intracellular dynamics from single diffraction-limited 2D light-filed measurements.

Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-Similarity.

Most existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods mainly utilize the property of the image or rain layers independently, while few of them have considered their mutually exclusive relationship.

Nonlinear Deblurring for Low-Light Saturated Image.

Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images.

Learning Oriented Object Detection via Naive Geometric Computing.

Detecting oriented objects along with estimating their rotation information is one crucial step for image analysis, especially for remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g.

Direction and Residual Awareness Curriculum Learning Network for Rain Streaks Removal.

Single-image rain streaks' removal has attracted great attention in recent years. However, due to the highly visual similarity between the rain streaks and the line pattern image edges, the over-smoothing of image edges or residual rain streaks' phenomenon may unexpectedly occur in the deraining results. To overcome this problem, we propose a direction and residual awareness network within the curriculum learning paradigm for the rain streaks' removal.

Improvement of Colloidal Characteristics in a Precursor Solution by a PbI-(DMSO) Complex for Efficient Nonstoichiometrically Prepared CsPbIBr Perovskite Solar Cells.

The optoelectronic properties of all-inorganic perovskite solar cells are greatly affected by the quality characteristics of films, such as the defect concentration, crystal growth orientation, crystallinity, and morphology. In this study, a PbI-(DMSO) complex is adopted to partially replace PbI as the lead source in the preparation of perovskite precursor solutions. Due to the rapid dispersion of the PbI-(DMSO) complex in a solvent, raw materials can rapidly react to form perovskite colloids with a narrow size distribution.

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration.

Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such as noises (random noise), blurs (Gaussian and uniform blur), and downsampled (both spectral and spatial downsample), each corresponding to the HSI denoising, deblurring, and super-resolution tasks, respectively. Previous HSI restoration methods are designed for one specific task only.

A variation-based ring artifact correction method with sparse constraint for flat-detector CT.

The reconstructed slice quality of flat-detector computed tomography (CT) is often disturbed by concentric-ring artifacts. Since concentric rings in CT slices appear as straight stripes when transformed into polar coordinates, a variation-based model is proposed to suppress the stripes. The method is motivated by two observations about stripes in polar coordinates: (1) ring artifacts attenuate gradually along the radial direction, leading to a sparse distribution of stripes and (2) stripes greatly distort the image gradient across the stripes, while slightly affecting the image gradient along the stripes.

Joint baseline-correction and denoising for Raman spectra.

Laser instruments often suffer from the problem of baseline drift and random noise, which greatly degrade spectral quality. In this article, we propose a variation model that combines baseline correction and denoising. First, to guide the baseline estimation, morphological operations are adopted to extract the characteristics of the degraded spectrum.

Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping.

Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner.

Robust destriping method with unidirectional total variation and framelet regularization.

Multidetector imaging systems often suffer from the problem of stripe noise and random noise, which greatly degrade the imaging quality. In this paper, we propose a variational destriping method that combines unidirectional total variation and framelet regularization. Total-variation-based regularizations are considered effective in removing different kinds of stripe noise, and framelet regularization can efficiently preserve the detail information.

Spatial-spectral method for classification of hyperspectral images.

Spatial-spectral approach with spatially adaptive classification of hyperspectral images is proposed. The rotation-invariant spatial texture information for each object is exploited and incorporated into the classifier by using the modified local Gabor binary pattern to distinguish different types of classes of interest. The proposed method can effectively suppress anisotropic texture in spatially separate classes as well as improve the discrimination among classes.

Blind Poissonian images deconvolution with framelet regularization.

We propose a maximum a posteriori blind Poissonian images deconvolution approach with framelet regularization for the image and total variation (TV) regularization for the point spread function. Compared with the TV based methods, our algorithm not only suppresses noise effectively but also recovers edges and detailed information. Moreover, the split Bregman method is exploited to solve the resulting minimization problem.

Semi-blind spectral deconvolution with adaptive Tikhonov regularization.

Deconvolution has become one of the most used methods for improving spectral resolution. Deconvolution is an ill-posed problem, especially when the point spread function (PSF) is unknown. Non-blind deconvolution methods use a predefined PSF, but in practice the PSF is not known exactly.

Blind image deconvolution with spatially adaptive total variation regularization.

A blind deconvolution algorithm with spatially adaptive total variation regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information.

A MAP-based algorithm for spectroscopic semi-blind deconvolution.

Spectroscopic data often suffer from common problems of bands overlapping and random noise. In this paper, we show that the issue of overlapping peaks can be considered as a maximum a posterior (MAP) problem and be solved by minimizing an object functional that includes a likelihood term and two prior terms. In the MAP framework, the likelihood probability density function (PDF) is constructed based on a spectral observation model, a robust Huber-Markov model is used as spectra prior PDF, and the kernel prior is described based on a parametric Gaussian function.

Saliency model for object detection: searching for novel items in the scene.

This Letter presents a new computational model of visual saliency. A new definition for saliency is proposed: saliency is novelty, which guides the deployment of visual attention. We define novelty as coming from regions that contain dissimilarities from the global scene.