Owing to the limitations of the imaging principle as well as the properties of imaging systems, infrared images often have some drawbacks, including low resolution, a lack of detail, and indistinct edges. Therefore, it is essential to improve infrared image quality. Considering the information of neighbors, a description of sparse edges, and by avoiding staircase artifacts, a new super-resolution reconstruction (SRR) method is proposed for infrared images, which is based on fractional order total variation (FTV) with quaternion total variation and the $L_p$ quasinorm. Our proposed method improves the sparsity exploitation of FTV, and efficiently preserves image structures. Furthermore, we adopt the plug-and-play alternating direction method of multipliers (ADMM) and the fast Fourier transform (FFT) theory for the proposed method to improve the efficiency and robustness of our algorithm; in addition, an accelerated step is adopted. Our experimental results show that the proposed method leads to excellent performances in terms of an objective evaluation and the subjective visual effect.

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.

The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature ( $T_{\mathrm {D}}$ ) and the target wet-bulb temperature ( $T_{\mathrm {W}}$ ) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting $T_{\mathrm {D}}$ and $T_{\mathrm {W}}$ . Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 °C and 0.8581 °C respectively. The promising results demonstrate that TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy of $T_{\mathrm {D}}$ and $T_{\mathrm {W}}$ was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.