Single image super-resolution (SISR) is an effective technique to improve the
quality of low-resolution thermal images. Recently, transformer-based methods
have achieved significant performance in SISR. However, in the SR task, only a
small number of pixels are involved in the transformers self-attention (SA)
mechanism due to the computational complexity of the attention mechanism. The
lambda abstraction is a promising alternative to SA in modeling long-range
interactions while being computationally more efficient. This paper presents
lambda abstraction-based thermal image super-resolution (LATIS), a novel
lightweight architecture for SISR of thermal images. LATIS sequentially
captures local and global information using the local and global feature block
(LGFB). In LGFB, we introduce a global feature extraction (GFE) module based on
the lambda abstraction mechanism, channel-shuffle and convolution (CSConv)
layer to encode local context. Besides, to improve the performance further, we
propose a differentiable patch-wise histogram-based loss function. Experimental
results demonstrate that our LATIS, with the least model parameters and
complexity, achieves better or comparable performance with state-of-the-art
methods across multiple datasets.

单幅图像超分辨率（SISR）的关键词包括：单幅图像超分辨率、基于 Transformer 的方法、lambda 抽象、热像。本文提出了一种基于 lambda 抽象的热像超分辨率新型轻量级架构（LATIS），通过局部和全局特征块（LGFB）分别捕获局部和全局信息，使用全局特征提取（GFE）模块进行全局特征提取，通过不同 iable patch-wise histogram-based loss function 进一步提高性能。实验结果表明，我们提出的 LATIS 在多个数据集上以最少的模型参数和复杂度实现了与其他最先进方法相媲美或更好的性能。