6 research outputs found
Revising regularisation with linear approximation term for compressive sensing improvement
In this Letter, the authors propose a novel revised regularisation to improve the performance of compressive sensing (CS) reconstruction. They suppose that a specific regularisation term is insufficient to accommodate the prior information of CS while it can be improved by further imposing a linear approximation term. They also prove that the revised regularisation is substantially equivalent to the CS preprocessing methods. They conduct extensive experiments on various CS algorithms, which show the effectiveness of their revised regularisation
Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement
Low-light image enhancement strives to improve the contrast, adjust the
visibility, and restore the distortion in color and texture. Existing methods
usually pay more attention to improving the visibility and contrast via
increasing the lightness of low-light images, while disregarding the
significance of color and texture restoration for high-quality images. Against
above issue, we propose a novel luminance and chrominance dual branch network,
termed LCDBNet, for low-light image enhancement, which divides low-light image
enhancement into two sub-tasks, e.g., luminance adjustment and chrominance
restoration. Specifically, LCDBNet is composed of two branches, namely
luminance adjustment network (LAN) and chrominance restoration network (CRN).
LAN takes responsibility for learning brightness-aware features leveraging
long-range dependency and local attention correlation. While CRN concentrates
on learning detail-sensitive features via multi-level wavelet decomposition.
Finally, a fusion network is designed to blend their learned features to
produce visually impressive images. Extensive experiments conducted on seven
benchmark datasets validate the effectiveness of our proposed LCDBNet, and the
results manifest that LCDBNet achieves superior performance in terms of
multiple reference/non-reference quality evaluators compared to other
state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure
Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing
The learned denoising-based approximate message passing (LDAMP) algorithm has
attracted great attention for image compressed sensing (CS) tasks. However, it
has two issues: first, its global measurement model severely restricts its
applicability to high-dimensional images, and its block-based measurement
method exhibits obvious block artifacts; second, the denoiser in the LDAMP is
too simple, and existing denoisers have limited ability in detail recovery. In
this paper, to overcome the issues and develop a high-performance LDAMP method
for image block compressed sensing (BCS), we propose a novel sparsity and
coefficient permutation-based AMP (SCP-AMP) method consisting of the
block-based sampling and the two-domain reconstruction modules. In the sampling
module, SCP-AMP adopts a discrete cosine transform (DCT) based sparsity
strategy to reduce the impact of the high-frequency coefficient on the
reconstruction, followed by a coefficient permutation strategy to avoid block
artifacts. In the reconstruction module, a two-domain AMP method with DCT
domain noise correction and pixel domain denoising is proposed for iterative
reconstruction. Regarding the denoiser, we proposed a multi-level deep
attention network (MDANet) to enhance the texture details by employing
multi-level features and multiple attention mechanisms. Extensive experiments
demonstrated that the proposed SCP-AMP method achieved better reconstruction
accuracy than other state-of-the-art BCS algorithms in terms of both visual
perception and objective metrics.Comment: The content modification has been upgraded and corrected on a large
scale, and request to withdraw this versio
Deformable channel nonâlocal network for crowd counting
Abstract Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel nonâlocal network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two wellâcrafted designed modules: deformable channel nonâlocal block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes longârange dependencies between pixels and the adaptive local correlation with channel nonâlocal and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the crossâlevel information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models
UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring
Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint airâground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV airâground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 ÎŒg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water