CTT: CNN Meets Transformer for Tracking

Siamese networks are one of the most popular directions in the visual object tracking based on deep learning. In Siamese networks, the feature pyramid network (FPN) and the cross-correlation complete feature fusion and the matching of features extracted from the template and search branch, respectively. However, object tracking should focus on the global and contextual dependencies. Hence, we introduce a delicate residual transformer structure which contains a self-attention mechanism called encoder-decoder into our tracker as the part of neck. Under the encoder-decoder structure, the encoder promotes the interaction between the low-level features extracted from the target and search branch by the CNN to obtain global attention information, while the decoder replaces cross-correlation to send global attention information into the head module. We add a spatial and channel attention component in the target branch, which can further improve the accuracy and robustness of our proposed model for a low price. Finally, we detailly evaluate our tracker CTT on GOT-10k, VOT2019, OTB-100, LaSOT, NfS, UAV123 and TrackingNet benchmarks, and our proposed method obtains competitive results with the state-of-the-art algorithms

Medical Image Fusion Based on Feature Extraction and Sparse Representation

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods

Compressed sensing based on the improved wavelet transform for image processing

Compressed sensing theory is a new sampling theory that can sample signal in a below sampling rate than the traditional Nyquist sampling theory. Compressed sensing theory that has given a revolutionary solution is a novel sampling and processing theory under the condition that the signal is sparse or compressible. This paper investigates how to improve the theory of CS and its application in imaging system. According to the properties of wavelet transform sub-bands, an improved compressed sensing algorithm based on the single layer wavelet transform was proposed. Based on the feature that the most information was preserved on the low-pass layer after the wavelet transform, the improved compressed sensing algorithm only measured the low-pass wavelet coefficients of the image but preserving the high-pass wavelet coefficients. The signal can be restricted exactly by using the appropriate reconstruction algorithms. The reconstruction algorithm is the key point that most researchers focus on and significant progress has been made. For the reconstruction, in order to improve the orthogonal matching pursuit (OMP) algorithm, increased the iteration layers make sure low-pass wavelet coefficients could be recovered by measurements exactly. Then the image could be reconstructed by using the inverse wavelet transform. Compared the original compressed sensing algorithm, simulation results demonstrated that the proposed algorithm decreased the processed data, signal processed time decreased obviously and the recovered image quality improved to some extent. The PSNR of the proposed algorithm was improved about 2 to 3 dB. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed

High Resolution and Fast Processing of Spectral Reconstruction in Fourier Transform Imaging Spectroscopy

High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher performance. At the same time, other solving algorithms for the modern spectrum estimation model are introduced for discussion and comparison. We compare traditional high-resolution solving algorithms running on the central processing unit (CPU) and the parallel algorithm on the GPU for processing the interferogram. The experimental results illustrate that runtime is reduced by about 70% using our parallel solution, and the GPU has a great advantage in processing large data and accelerating applications

Sub-Diffraction Visible Imaging Using Macroscopic Fourier Ptychography and Regularization by Denoising

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications

A method of small moving target detection

This paper present a new method for detecting small moving targets from sequential images under the complex background of sky. The method can identify moving targets in low SNR, estimate and track the trajectory of the target using the maximum gray matrix and Kalman filter theory. Compared with other algorithms, this method has features of small amount of calculation, fast detecting speed, high real-time performance, and small identification error. Experimental results show that the pepper noise and fixed pattern noise in this type of image sequence have a good filtering effect, and also our method has good recognition and tracking effect for the small moving targets. Copyright © 2014 Binary Information Press

Research and Design of Color Restoration Algorithm for Demosaicing Bayer Pattern Images

This paper analyses the process of obtaining real color image based on a single CCD/CMOS sensor. Bayer CFA Interpolation algorithm and White Balance algorithm are presented. The proposed interpolation algorithm which can obtain better color image and is easier to implement by hardware is provided. In order to cancel chromatic aberration, one White Balance algorithm which aims at sequential image is presented and process of FPGA design is given. Experimental results show that the design works normally. The color of corrected picture is vivid compared to the real world

Effects of Panax Notoginseng Saponins on Esterases Responsible for Aspirin Hydrolysis In Vitro

Herb–drug interactions strongly challenge the clinical combined application of herbs and drugs. Herbal products consist of complex pharmacological-active ingredients and perturb the activity of drug-metabolizing enzymes. Panax notoginseng saponins (PNS)-based drugs are often combined with aspirin in vascular disease treatment in China. PNS was found to exhibit inhibitory effects on aspirin hydrolysis using Caco-2 cell monolayers. In the present study, a total of 22 components of PNS were separated and identified by UPLC-MS/MS. Using highly selective probe substrate analysis, PNS exerted robust inhibitory potency on human carboxylesterase 2 (hCE2), while had a minor influence on hCE1, butyrylcholinesterase (BChE) and paraoxonase (PON). These effects were also verified through molecular docking analysis. PNS showed a concentration-dependent inhibitory effect on hydrolytic activity of aspirin in HepaRG cells. The protein level of hCE2 in HepaRG cells was suppressed after PNS treatment, while the level of BChE or PON1 in the extracellular matrix were elevated after PNS treatment. Insignificant effect was observed on the mRNA expression of the esterases. These findings are important to understand the underlying efficacy and safety of co-administration of PNS and aspirin in clinical practice