Adaptive sampling and reconstruction for gradient-domain rendering

In this paper, we improve the trilateral filter for removing the mix of Gaussian and impulse noise. The new algorithm incorporates Rank-Ordered Absolute Differences (ROAD) Statistic for detecting outliers in gradient domain and intensity domain of image with impulse noise. A switching mechanism is adopted in gradient bilateral filter and intensity bilateral filter for smoothing the gradients and intensities of impulse noise samples and impulse noise-free samples with different parameters. By introduce the impulse detector in both gradient domain and intensity domain, the proposed algorithm has demonstrated superior performance in suppressing noise, which include Gaussian, impulse, and mixed noise. Compared to most other nonlinear filters, the proposed algorithm consistently yields good results in a sharply-bounded, gradient piecewise-linear approximation which provides stronger noise reduction and better edge-limited smoothing behavior.

Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered...

Single-shot Fresnel incoherent correlation holography based on digital self-calibrated point source holograms

The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 × 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects.

In this paper, we introduce a novel signal characteristic, which is ubiquitous in the vast majority of communication signals. That is, a modulated signal is of an inherent low-rank structure following a reshaping operation. We first use a toy model to develop a framework for modelling this signal characteristic, and theoretically prove the impact on the signals’ rank structure by additive white Gaussian noise and inter-symbol interference, which is of great concern in the wireless communication field. Subsequently, the model is generalized to multi-input-multi-output signals, and tensor rank is taken into account. Using multi-linear algebra, we prove that the low-rankness of a reshaped signal only depends on the structure of its embedding subspace, and that its rank measure is upper bounded by the multi-rank of the basis tensor. As an application, we propose a novel adaptive sampling and reconstruction scheme for generic software-defined radio based on the low-rank structure. Numerical simulations demonstrate that the proposed method outperforms compressed sensing-based method, particularly when the modulated signal does not satisfy the sparsity assumption in the time and frequency domains. The results of practical experiments further demonstrate that many types of modulated signals can be effectively reconstructed from very limited observations using our proposed method.

This article proposes a novel and accurate method for matching face-sketch images with face-photo images, and vice versa. A new local fuzzy descriptor in the gradient domain, called the local gradient fuzzy pattern (LGFP), is proposed. The LGFP is a set of membership values generated using the restricted equivalence function (REF) of the intensity (gradient value) differences between a central pixel and its neighbors in a local 3 x 3 window. The LGFP outperforms other fuzzy domain-related local binary features. It was tested on blurred, noisy, and disguised sketches, where it established its robustness and superiority over established recent methods.

The technology of super-resolution reconstruction has been widely used in various fields, but there are still significant challenges in reconstructing Digital Elevation Model (DEM). To overcome the problems of losing details and distorting the complex terrain features of DEM, we have developed a new model called Edge Feature Transformer-based DEM Super-Resolution Network (EF-TSR). Our model enhances the ability to capture overall information and model long-range dependencies using a series of residual feature fusion blocks. Within these blocks, the multi-scale pyramid segmentation Transformer layer integrates multi-head self-attention with pyramid segmentation attention, effectively capturing both local and global DEM features. Additionally, to improve the realism of reconstructed DEM, we include a dual-filter convolution block that extracts high- and low-frequency features through two parallel filter structures. The super-resolution reconstruction process is guided by dual constraints in the gradient and height domains. Experimental results using elevation maps of Shaanxi Qinling at two different resolutions show that the EF-TSR model outperforms other advanced models across various metrics, producing DEM with richer details and clearer textures. In summary, the EF-TSR model proposed in this study not only enhances the quality of DEM reconstruction but also provides new perspectives and methodologies for further research in related fields.

Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in remote sensing applications. Recent studies have demonstrated that reference image-based reconstruction methods have great potential for higher reconstruction performance, while lacking accuracy and quality of reconstruction. For this application, a new compressed sensing objective function incorporating a reference image as prior information is developed. We resort to the reference prior information inherent in interior and exterior data simultaneously to build a new generalized nonconvex low-rank approximation framework for RSI reconstruction. Specifically, the innovation of this paper consists of the following three respects: (1) we propose a nonconvex low-rank approximation for reconstructing RSI; (2) we inject reference prior information to overcome over smoothed edges and texture detail losses; (3) on this basis, we combine conjugate gradient algorithms and a single-value threshold (SVT) simultaneously to solve the proposed algorithm. The performance of the algorithm is evaluated both qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm improves several dBs in terms of peak signal to noise ratio (PSNR) and preserves image details significantly compared to most of the current approaches without reference images as priors. In addition, the generalized nonconvex low-rank approximation of our approach is naturally robust to noise, and therefore, the proposed algorithm can handle low resolution with noisy inputs in a more unified framework.

Most existing 3D reconstruction methods result in either detail loss or unsatisfying efficiency. However, effectiveness and efficiency are equally crucial in real-world applications, e.g., autonomous driving and augmented reality. We argue that this dilemma comes from wasted resources on valueless depth samples. This paper tackles the problem by proposing a novel learning-based 3D reconstruction framework named Dionysus. Our main contribution is to find out the most promising depth candidates from estimated semantic maps. This strategy simultaneously enables high effectiveness and efficiency by attending to the most reliable nominators. Specifically, we distinguish unreliable depth candidates by checking the cross-view semantic consistency and allow adaptive sampling by redistributing depth nominators among pixels. Experiments on the most popular datasets confirm our proposed framework's effectiveness.

A new temporal sampling method for human action recognition using depth maps is proposed. The depth map frames are projected onto three orthogonal Cartesian planes respectively and the absolute difference between two successive projected ones is stacked to form the motion energy. Three sub-action frame sequences are selected accordingly, which constructs Adaptive Temporal Sampling (ATS) descriptor, capturing the dynamic features of one action. In order to represent the static features, the Depth Static Map (DSM) is adopted to constitute ATS-DSM descriptor. An l 2 -regularized collaborative representation classifier is applied. The developed approach is proven to be computationally efficient and the experimental results on the testing dataset outperform the state-of-the-art performance.

Detailed scanning of indoor scenes is tedious for humans. We propose autonomous scene scanning by a robot to relieve humans from such a laborious task. In an autonomous setting, detailed scene acquisition is inevitably coupled with scene analysis at the required level of detail. We develop a framework for object-level scene reconstruction coupled with object-centric scene analysis. As a result, the autoscanning and reconstruction will be object-aware , guided by the object analysis. The analysis is, in turn, gradually improved with progressively increased object-wise data fidelity. In realizing such a framework, we drive the robot to execute an iterative analyze-and-validate algorithm which interleaves between object analysis and guided validations. The object analysis incorporates online learning into a robust graph-cut based segmentation framework, achieving a global update of object-level segmentation based on the knowledge gained from robot-operated local validation. Based on the current analysis, the robot performs proactive validation over the scene with physical push and scan refinement, aiming at reducing the uncertainty of both object-level segmentation and object-wise reconstruction. We propose a joint entropy to measure such uncertainty based on segmentation confidence and reconstruction quality, and formulate the selection of validation actions as a maximum information gain problem. The output of our system is a reconstructed scene with both object extraction and object-wise geometry fidelity.

The study presents a novel phase-contrast MRI (PC-MRI) reconstruction framework, employing spatial-temporal dictionary learning neural networks (stDLNN), to tackle the issues of artifacts and noise in undersampled radial acquisitions of dynamic PC images. By integrating dictionary learning and deep learning, the proposed method aims to improve reconstruction accuracy. Comparative results reveal the stDLNN-based approach outperforms existing methods in velocity-encoded image reconstruction and velocity map quality, even at high acceleration rates. This advancement in PC-MRI reconstruction holds promise for real-time imaging, with potential implications for enhancing clinical applications and advancing PC-MRI reconstruction techniques.

Image super-resolution (SR) reconstruction has been a hot research topic in recent years. This technique allows the recovery of a high-resolution (HR) image from several low-resolution (LR) images that are noisy, blurred and down-sampled. Among the available reconstruction frameworks, the maximum a posteriori (MAP) model is widely used. In this model, the regularization parameter plays an important role. If the parameter is too small, the noise will not be effectively restrained; conversely, the reconstruction result will become blurry. Therefore, how to adaptively select the optimal regularization parameter has been widely discussed. In this paper, we propose an adaptive MAP reconstruction method based upon a U-curve. To determine the regularization parameter, a U-curve function is first constructed using the data fidelity term and prior term, and then the left maximum curvature point of the curve is regarded as the optimal parameter. The proposed algorithm is tested on both simulated and actual data. Experimental results show the effectiveness and robustness of this method, both in its visual effects and in quantitative terms.

New PFG NMR Spectrometer with a Rotatable Quadrupole Coil for the Measurement of an Anisotropic Self-Diffusion Coefficient Tensor