Generative Deep Learning-Based Thermographic Inspection of Artwork

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

Maintenance of abiotic stress memory in plants: Lessons learned from heat acclimation

Plants acquire enhanced tolerance to intermittent abiotic stress by employing information obtained during prior exposure to an environmental disturbance, a process known as acclimation or defense priming. The capacity for stress memory is a critical feature in this process. The number of reports related to plant stress memory (PSM) has recently increased, but few studies have focused on the mechanisms that maintain PSM. Identifying the components involved in maintaining PSM is difficult due in part to the lack of clear criteria to recognize these components. In this review, based on what has been learned from genetic studies on heat acclimation memory, we propose criteria for identifying components of the regulatory networks that maintain PSM. We provide examples of the regulatory circuits formed by effectors and regulators of PSM. We also highlight strategies for assessing PSMs, update the progress in understanding the mechanisms of PSM maintenance, and provide perspectives for the further development of this exciting research field.