Infrared image super-resolution reconstruction technology can improve image resolution without changing the hardware of the imaging system, and has high cost-effectiveness. In this paper, a super-resolution reconstruction method based on convolutional neural network and pixel shuffle is proposed for the variable length infrared image sequences. Global residual learning and local residual block are introduced to accelerate the convergence speed of the network. Non-local residual block, progressive fusion residual blocks and pixel shuffle module are used to learn the long-distance time information and rich spatial information of infrared low-resolution image sequences. In addition to the fidelity evaluation indexes commonly used in current representative super-resolution reconstruction methods, we also introduce visual perception and image sharpness evaluation functions for perceptual evaluation. The network in this paper is trained and tested on real-world multi-frame infrared images. The experimental results show that the proposed method has advantages in obtaining better perception quality.
Hyperspectral images provide significant spatial and spectral information which are widely used in object detection. Two-stage detectors are commonly employed in hyperspectral object detection, where effective region proposals play a crucial role in accurate object localization. However, during non-maximum suppression (NMS) process, the Intersection over Union (IoU) metric based solely on spatial geometric information is inadequate for discriminating between similar proposals. This results in a substantial number of expected proposals with dissimilar characteristics are eliminated. In this paper, we analyze the spectral information in hyperspectral images to distinguish the characteristics of different proposals. Furthermore, this paper proposes the Spectral IoU (SIoU) by introducing spectral signature differences as a new metric. This improves the ability to differentiate between different object instances and increases the recall rate of bounding boxes with high localization confidence in region proposal stage. Moreover, SIoU can be simply integrated into the hyperspectral objection detection frameworks without introducing additional computational complexity. Extensive experiments on the Semi-Supervised Hyperspectral Object Detection Challenge dataset demonstrate the effectiveness of our method.
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