Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sensors. In this paper, we propose a dynamic attention mechanism-based thermal image super-resolution network for infrared sensors. Specifically, the dynamic attention modules adaptively reweight the outputs of the attention and non-attention branches according to features at different depths of the network. The attention branch, which consists of channel- and pixel-wise attention blocks, is responsible for extracting the most informative features, while the non-attention branch is adopted as a supplement to extract the remaining ignored features. The dynamic weights block operates with 1D convolution instead of the full multi-layer perceptron on the global average pooled features, reducing parameters and enhancing information interaction between channels, and the same structure is adopted in the channel attention block. Qualitative and quantitative results on three testing datasets demonstrate that the proposed network can superior restore high-frequency details while improving the resolution of thermal images. And the lightweight structure of the proposed network with lower computing cost can be practically deployed on edge devices, effectively improving the imaging perception quality of infrared sensors.

Color constancy is used to determine the actual surface color of the scene affected by illumination so that the captured image is more in line with the characteristics of human perception. The well-known Gray-Edge hypothesis states that the average edge difference in a scene is achromatic. Inspired by the Gray-Edge hypothesis, we propose a new illumination estimation method. Specifically, after analyzing three public datasets containing rich illumination conditions and scenes, we found that the ratio of the global sum of reflectance differences to the global sum of locally normalized reflectance differences is achromatic. Based on this hypothesis, we also propose an accurate color constancy method. The method was tested on four test datasets containing various illumination conditions (three datasets in a single-light environment and one dataset in a multi-light environment). The results show that the proposed method outperforms the state-of-the-art color constancy methods. Furthermore, we propose a new framework that can incorporate current mainstream statistics-based color constancy methods (Gray-World, Max-RGB, Gray-Edge, etc.) into the proposed framework.