LATIS: Lambda Abstraction-based Thermal Image Super-resolution
This work addresses the computational inefficiency of transformers in super-resolution tasks for thermal imaging, offering a more efficient solution for applications like surveillance or medical imaging.
The paper tackles the problem of super-resolution for thermal images by proposing LATIS, a lightweight architecture that uses lambda abstraction to model long-range interactions more efficiently than transformers, achieving better or comparable performance with state-of-the-art methods while having the least model parameters and complexity.
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.