Blind Restoration of Atmospheric Turbulence-Degraded Images Based on Curriculum Learning

Atmospheric turbulence-degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a Noise Suppression-based Restoration Network (NSRN) for turbulence-degraded images, in which the noise suppression module is designed to learn low-rank subspaces from turbulence-degraded images, the attention-based asymmetric U-NET module is designed for blurred-image deconvolution, and the Fine Deep Back-Projection (FDBP) module is used for multi-level feature fusion to reconstruct a sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve superior performance through a local-to-global, easy-to-difficult learning method. Based on NSRN, we achieve state-of-the-art performance with PSNR of 30.1 dB and SSIM of 0.9 on the simulated dataset and better visual results on the real images

Blind Restoration of Atmospheric Turbulence-Degraded Images Based on Curriculum Learning

Atmospheric turbulence-degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a Noise Suppression-based Restoration Network (NSRN) for turbulence-degraded images, in which the noise suppression module is designed to learn low-rank subspaces from turbulence-degraded images, the attention-based asymmetric U-NET module is designed for blurred-image deconvolution, and the Fine Deep Back-Projection (FDBP) module is used for multi-level feature fusion to reconstruct a sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve superior performance through a local-to-global, easy-to-difficult learning method. Based on NSRN, we achieve state-of-the-art performance with PSNR of 30.1 dB and SSIM of 0.9 on the simulated dataset and better visual results on the real images

Physicochemical, taste, and functional changes during the enhanced fermentation of low-salt Sufu paste, a Chinese fermented soybean food

In this study, the physicochemical, taste, and functional changes in low-salt Sufu paste were investigated during fermentation as enhanced by a mixed starter (Pichia fermentans, Kodamaea ohmeri, and Lactococcus lactis subsp. lactis) and α-ketoglutarate. The total free amino acids increased from 0.02 mg/g dry matter to 12.63 mg/g dry matter, and monosodium glutamate-like was the major free amino acid group (3.89 mg/g dry matter), followed by bitter according to the taste characteristics. Functional components such as γ-aminobutyric acid, riboflavin (VB2), and puerarin significantly increased to 30.96, 4.97, and 12.03 mg/g dry matter, respectively, at the end of post-ripening (p < 0.05). Protease, lipase, peptidase, α-amylase, aromatic amino acid aminotransferase, and branched-chain amino acid aminotransferase activities increased and were significantly correlated (p < 0.05) with most physicochemical and functional components, indicating that enzymes may play an important role in the fermentation process of Sufu paste. Additionally, the color of the Sufu paste changed from pale yellow to yellowish brown, whereas the Sufu paste fermented without the mixed starter and α-ketoglutarate changed to gray, the unique color of gray Sufu. The preliminary results indicated that the mixed starter and α-ketoglutarate are beneficial for taste quality and color and that fermentation may be an effective method to enhance the functional components in Sufu paste. This information would be useful for future improvements in the manufacturing process and quality of Sufu paste

Parameter Estimation for the Field Strength of Radio Environment Maps

The parameters of a radio environment map play an important role in radio management and cognitive radio. In this paper, a method for estimating the parameters of the radio environment map based on the sensing data of monitoring nodes is presented. According to the principles of radio transmission signal intensity losses, a theoretical variogram model based on a propagation model is proposed, and the improved theoretical variation function is more in line with the attenuation of radio signal propagation. Furthermore, a weight variogram fitting method is proposed based on the characteristics of field strength parameter estimation. In contrast to the traditional method, this method is more closely related to the physical characteristics of the electromagnetic environment parameters, and the design of the variogram and fitting method is more in line with the spatial distribution of electromagnetic environment parameters. Experiments on real and simulation data show that the proposed method performs better than the state-of-the-art method

ANLPT: Self-Adaptive and Non-Local Patch-Tensor Model for Infrared Small Target Detection

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive

Comprehensive explorations of nutritional, functional and potential tasty components of various types of Sufu, a Chinese fermented soybean appetizer

Abstract The nutritional, functional and tasty components of three categories of Sufu, divided by strains in the culture phase, were comprehensively investigated in this study. The levels of isoflavones, γ-aminobutyric acid (GABA), phytosterols and soyasaponin were 0.58-2.20, 7.46-57.95, 0.73-2.72 and 10.89-23.35 mg/g dry matter (DM), respectively. Glu was the most abundant of the 17 detected free amino acids (FAAs), followed by Phe, Leu, Val and Asp. Additionally, potential tasty peptide profiles were typed by the segment of molecular weight (MW) < 300 Da, ranging from 79.22% ± 4.12% to 95.24% ± 2.93%. The complex taste impression based on the electronic tongue showed that the bitterness intensity was the highest, which was followed by saltiness and the umami intensity. To some extent, different Sufu categories can be distinguished according to the electronic tongue. The results provide a theoretical basis for improving the quality control and standardization of the manufacturing process

Surface Integrity Characteristics and Fatigue Failure Mechanism of Carburized M50NiL Steel

The surface integrity of carburized M50NiL steel was studied by optical microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), microhardness tester and residual stress tester. The fatigue properties of the two specimens were measured by the rotational bending fatigue test, and the fatigue test results were simulated and analyzed. The results show that the rotation bending fatigue of carburized M50NiL steel is originated in the sub-surface in the ideal case without considering the surface processing defects. The surface stress concentration factor produced by general grinding causes the fatigue source to be moved from the surface to the sub-surface. Precise grinding improves the surface quality by optimizing the grinding process, effectively restrains the stress concentration of the working surface, and returns the fatigue source from the surface to the sub-surface. The maximum rotary bending fatigue life can be increased by 30 times and the average is 15 times