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11.
Most low-light image enhancement methods only adjust the brightness, contrast and noise reduction of low-light images, making it difficult to recover the lost information in darker areas of the image, and even cause color distortion and blurring. To solve the above problems, a global attention-based Retinex network (GARN) for low-light image enhancement is proposed in this paper. We propose a novel global attention module which computes multiple dimensional information in the channel attention module to help facilitate inference learning. Then the global attention module is embedded into different layers of the network to extract richer shallow texture features and deep semantic features. This means that the rich features are more conducive to learning the mapping relationship between low-light images to normal-light images, so that the detail recovery of dark regions is enhanced in low-light images. We also collected a low/normal light image dataset with multiple scenes, in which the images paired as training set can succeed to be applied to low-light image enhancement under different lighting conditions. Experimental results on publicly available datasets show that our method has better effectiveness and generality than the state-of-the-art methods in terms of evaluations metrics such as PSNR, SSIM, NIQE, Entropy. 相似文献
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文章提出了一种宽带注入锁定三倍频器。在传统注入方式基础上,倍频器采用了推-推差分对输入信号进行二倍频,并将产生的二次谐波通过变压器耦合至注入管的源极共模点,增强了注入管源极共模点二次谐波。由于注入电流是由注入信号与源极共模点二次谐波进行混频而产生,因此注入电流也被增强,从而增大了锁定范围。除此之外,三倍频采用了四阶谐振器,谐振阻抗的相位在过零点被平坦化,锁定范围进一步被增大。采用标准CMOS 65 nm工艺设计三倍频,芯片面积为720×670 μm2,1.2-V供电时的功耗为15.2 mW。0 dBm注入功率下三倍频的锁定范围为19.2~27.6 GHz,对应的基波抑制比大于25 dB,二次谐波抑制大于35 dB。注入锁定三倍频器可满足5G收发机中本振源的要求。 相似文献
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The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise, which leads to a reduction in image quality. A multi-scale fusion residual encoder-decoder (FRED) was proposed to solve the problem. By directly learning the end-to-end mapping between light and dark images, FRED can enhance the image's brightness with the details and colors of the original image fully restored. A residual block (RB) was added to the network structure to increase feature diversity and speed up network training. Moreover, the addition of a dense context feature aggregation module (DCFAM) made up for the deficiency of spatial information in the deep network by aggregating the context's global multi-scale features. The experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signa-to-noise ratio (PSNR) and structural similarity index measure (SSIM). For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively, a satisfactory visual quality is obtained under the enhancement of low-light. 相似文献
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讨论了一种基于传统谱相减算法的改进方法。利用语音的短时平稳性,通过先验幅度比来连续更新噪声谱的估计,从而代替复杂的VAD(话音活性检测)。计算机仿真结果表明,这种改进方法有效抑制了噪声干扰,语音得到了增强,在极大地提高信噪比的同时,将残留的音乐噪声和语音失真保持在人耳听觉容忍的范围以内,从而较好的保持了语音自然度。 相似文献
15.
State-of-the-art speech watermarking techniques enable speech signals to be authenticated and protected against any malicious attack to ensure secure speech communication. In general, reliable speech watermarking methods must satisfy four requirements: inaudibility, robustness, blind-detectability, and confidentiality. We previously proposed a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction (LP) scheme to solve the first two issues (inaudibility and robustness) due to distortion by spread spectrum. This method not only effectively embeds watermarks with small distortion but also has the same robustness as the DSS method. There are, however, two remaining issues with blind-detectability and confidentiality. In this work, we attempt to resolve these issues by developing an approach called the LP-DSS scheme, which takes two forms of data embedding for blind detection and frame synchronization. We incorporate blind detection with frame synchronization into the scheme to satisfy blind-detectability and incorporate two forms of data embedding process, front-side and back-side embedding for blind detection and frame synchronization, to satisfy confidentiality. We evaluated these improved processes by carrying out four objective tests (PESQ, LSD, Bit-error-rate, and accuracy of frame synchronization) to determine whether inaudibility and blind-detectability could be satisfied. We also evaluated all combinations with the two forms of data embedding for blind detection with frame synchronization by carrying out BER tests to determine whether confidentiality could be satisfied. Finally, we comparatively evaluated the proposed method by carrying out ten robustness tests against various processing and attacks. Our findings showed that an inaudible, robust, blindly detectable, and confidential speech watermarking method based on the proposed LP-DSS scheme could be achieved. 相似文献
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提出一种基于Transformer模型的通信信号调制识别方法:在数据准备阶段,构建一个不同符号速率调制识别(DSRMR)数据集;在数据预处理阶段,提出I/Q数据增强方法,用于满足模型训练在数量上和多样性的要求,增强了模型泛化能力;在模型构建阶段,将切片序列化的方法引入调制识别Transformer模型中,用于优化Transformer神经网络模型的输入问题。实验结果证明,基于Transformer模型的通信信号调制识别方法能够获得较高的信号自动调制识别准确率。 相似文献
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