LATIS: Lambda Abstraction-based Thermal Image Super-resolution
Gargi Panda Soumitra Kundu Saumik Bhattacharya Aurobinda Routray
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.