Low Resolution Thermal

Low-resolution thermal imaging research focuses on improving the quality, accuracy, and applicability of data from inexpensive thermal cameras. Current efforts concentrate on developing advanced deep learning models, including vision transformers and recurrent neural networks, to enhance image resolution, improve temperature accuracy, and enable efficient feature extraction for various applications. These advancements are significant because they enable the use of low-cost thermal sensors in diverse fields, such as industrial process monitoring, healthcare workload assessment, and precision agriculture, where high-resolution systems were previously cost-prohibitive. The development of computationally efficient algorithms, like those based on spiking neural networks, further expands the accessibility and practicality of low-resolution thermal imaging.