Super-resolution imaging quality enhancement method for distributed array infrared camera

Abstract To address issues related to low resolution and high noise in infrared cameras, a distributed array infrared camera imaging system utilizing four cameras is proposed. The four cameras are arranged in an unconstrained array, and the combination algorithm of Projections onto Convex Sets (POCS) and Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) is applied to achieve high-quality super-resolution infrared imaging. The wavelet fusion algorithm is used to preprocess four low-resolution infrared images to reduce noise. Then, the POCS algorithm is used to reconstruct the preprocessed image. Finally, the Real-ESRGAN is employed for image reconstruction, resulting in an ultra-high-resolution infrared image. The results show that compared to single infrared camera imaging, the resolution of images reconstructed using the distributed infrared camera array is increased by 0.58 times, with the Modulation Transfer Function (MTF) increased by 1.2 times. Additionally, the entropy is increased by 18.87%, the standard deviation is increased by 13.51%, and the Naturalness Image Quality Evaluator (NIQE) is reduced by 18.87%. This demonstrates a significant enhancement in the super-resolution imaging quality of the distributed infrared camera array.