LATIS: Lambda Abstraction-based Thermal Image Super-resolution

• URL: http://arxiv.org/abs/2311.12046v1
• Date: Sat, 18 Nov 2023 02:55:04 GMT
• Title: LATIS: Lambda Abstraction-based Thermal Image Super-resolution
• Authors: Gargi Panda, Soumitra Kundu, Saumik Bhattacharya, Aurobinda Routray
• Abstract summary: Single image super-resolution (SISR) is an effective technique to improve the quality of low-resolution thermal images. The abstraction-based thermal image super-resolution (LATIS) is a novel lightweight architecture for SISR of thermal images.
• Score: 10.375865762847347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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