Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.

Three-dimensional (3D) measurement of metal surfaces is one of fundamental tasks for product life-cycle management of complex equipment, which is meaningful but challenging due to its optical characteristics of highly reflectivity. To reliably reconstruct 3D metal surfaces, commonly-used techniques heavily rely on multiple exposures for optimal fusion but do not fit to high-efficient monitoring. To alleviate this reliance, we propose a novel single-exposure method called deep sinusoidal prior (DSP) for damaged phase recovery of highly reflective surfaces. Specifically, the sinusoidal hypothesis is instilled into an untrained deep neural network (DNN) as two-stream information, in order to bypass the problem of brightness enhancement. Utilizing elaborately designed loss functions, this approach enables us to restore the accurate phase encoding by fitting the DNN to two-stream sinusoidal priors. Experimental results demonstrate the proposed DSP method has superior performances on damaged phase recovery requiring no training samples. For instance, measuring a standard workpiece, absolute errors of the DSP method have been decreased substantially (81.69% and 59.49%) compared with the direct measurement and achieved similar accuracy (0.0754 mm vs. 0.0744 mm) compared with the reference. Most strikingly, the proposed method, for the first time, demonstrates a new perspective of recovering the reliable phase from a degraded one itself, contributing to the superior generalization capability insensitive to fringe frequencies, imaging settings and variant scenes.

The unfavorable shooting environment severely hinders the acquisition of actual landscape information in garden landscape design. Low quality, low illumination garden landscape images (GLIs) can be enhanced through advanced digital image processing. However, the current color enhancement models have poor applicability. When the environment changes, these models are easy to lose image details, and perform with a low robustness. Therefore, this paper tries to enhance the color of low illumination GLIs. Specifically, the color restoration of GLIs was realized based on modified dynamic threshold. After color correction, the low illumination GLI were restored and enhanced by a self-designed convolutional neural network (CNN). In this way, the authors achieved ideal effects of color restoration and clarity enhancement, while solving the difficulty of manual feature design in landscape design renderings. Finally, experiments were carried out to verify the feasibility and effectiveness of the proposed image color enhancement approach.