This paper presents an FPGA-based lightweight and real-time infrared image processor based on a series of hardware-oriented lightweight algorithms. The two-point correction algorithm based on blackbody radiation is introduced to calibrate the non-uniformity of the sensor. With precomputed gain and offset matrices, the design can achieve real-time non-uniformity correction with a resolution of $640\times 480$. The blind pixel detection algorithm employs the first-level approximation to simplify multiple iterative computations. The blind pixel compensation algorithm in our design is constructed on the side-window-filtering method. The results of eight convolution kernels for side windows are computed simultaneously to improve the processing speed. Due to the proposed side-window-filtering-based blind pixel compensation algorithm, blind pixels can be effectively compensated while details in the image are preserved. Before image output, we also incorporated lightweight histogram equalization to make the processed image more easily observable to the human eyes. The proposed lightweight infrared image processor is implemented on Xilinx XC7A100T-2. Our proposed lightweight infrared image processor costs 10,894 LUTs, 9367 FFs, 4 BRAMs, and 5 DSP48. Under a 50 MHz clock, the processor achieves a speed of 30 frames per second at the cost of 1800 mW. The maximum operating frequency of our proposed processor can reach 186 MHz. Compared with existing similar works, our proposed infrared image processor incurs minimal resource overhead and has lower power consumption.

Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively.