- 标题
- 摘要
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- 实验方案
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Efficient Variable Rate Image Compression with Multi-scale Decomposition Network
摘要： While deep learning image compression methods have shown impressive coding performance, most of them output a single optimized compression rate using a trained specific network. However, in practical it is essential to support variable rate compression or meet a target rate with high coding performance. This paper proposes a novel image compression method, making it possible for a single CNN model to generate variable rate efficiently with optimized rate-distortion (RD) performance. The method consists of CNN based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality-factor. For a target rate, the allocation is adaptive based on content complexity. And for a target quality-factor which indicates a trade-off between rate and quality, the optimal scale is determined by minimizing the RD cost. Experimental results have shown that our method has outperformed JPEG2000 and BPG standards with high efficiency and state-of-the-art RD performance as measured by MS-SSIM. Moreover, our method can strictly control the rate to generate the target compression result.
关键词： Convolutional neural network,Multi-scale decomposition transform,Content adaptive rate allocation,Lossy image compression,Variable rate image compression
更新于2025-09-04 15:30:14
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Visible active reduced graphene oxide loaded Titania for photodecomposition of ciprofloxacin and its antibacterial activity
摘要： The reduced Graphene Oxide based titanium dioxide (rGO-TiO2) nanocomposite was synthesized by a simple hydrothermal preparation and characterized by X-ray Diffraction Analysis (XRD), UV–Vis absorption spectroscopy (UV), Fourier Transform Infrared spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The XRD pattern of rGO-TiO2 indicates the presence of anatase TiO2 and average crystalline size of particles is 32 nm. The optical band gaps of TiO2, GO and rGO-TiO2 nanocomposite are 3.24 eV, 4.3 eV and 2.7 eV respectively. Comparison of efficiencies of three catalysts shows that ciprofloxacin degrades at a faster rate under visible light irradiation in the presence of rGO-TiO2 at 60 min than in presence of pure TiO2 commercial TiO2-P25. Higher photocatalytic decomposition efficiency of rGO-TiO2 is explained by its reduced electron-hole recombination and visible light activity. The kinetics of photodecomposition reaction was analyzed. Antibacterial activity analysis of rGO-TiO2 nanoparticles reveals that it is more active against S. aureus than E.coli.
关键词： Titania,Antibacterial activity,Photocatalytic decomposition,Ciprofloxacin,Reduced graphene oxide
更新于2025-09-04 15:30:14
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A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure
摘要： Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Speci?cally, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the ?nal fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.
关键词： texture information entropy,adaptive selection,multi-exposure image fusion,patch structure decomposition
更新于2025-09-04 15:30:14
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On the Use of Cross-Correlation between Volume Scattering and Helix Scattering from Polarimetric SAR Data for the Improvement of Ship Detection
摘要： Synthetic Aperture Radar (SAR) ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False Alarm Rate (CFAR). It is known that radar targets and azimuth ambiguities have different characteristics in polarimetric SAR (PolSAR) data, i.e., first ambiguities usually have strong odd- or double-bounce scattering and the maximum amplitude of the first ambiguity in SHV is always considerably smaller than that of the corresponding target for zero or high velocity. On the basis of this characteristics, this paper finds that first ambiguities usually have low volume scattering power relative to ships and almost have no helix scattering by Yamaguchi decomposition. But some residual ambiguities still exit in the volume scattering power and have similar scattering intensity to small ships, and some parts of a ship also have zero helix scattering owing to some physical factors (e.g., ship structure, radar incidence angle, etc.). Thus, for high-precision ship detection, a new ship detection method based on cross-correlation between the volume and helix scattering mechanisms derived from Yamaguchi decomposition is proposed to avoid false alarms caused by azimuth ambiguities and enhance Target-to-Clutter Ratio (TCR) for improving the miss detection rate of small ships. By experiments, it is proved that our method can work effectively and has high detection accuracy.
关键词： CFAR,azimuth ambiguity,polarimetric SAR,ship detection,Yamaguchi decomposition
更新于2025-09-04 15:30:14