Infrared (IR) images captured by the blast furnace (BF) top system suffer from severe degradation, including low contrast and texture loss, due to dust, steam, and thermal glare. These issues critically impair image-based monitoring and visual algorithms. Traditional IR enhancement methods only address simple noise, while visible light dehazing algorithms fail on pseudo-colour IR images with low contrast and unnatural textures. To overcome these limitations, we propose an IR image restoration transformer called IRReFormer, a deep fusion model targeting BF top IR image degradation characteristics. IRReFormer integrates the advantages of both traditional IR enhancement and visible light dehazing algorithms, incorporating three key components: (1) a frequency feature enhancement self-attention for improving global feature representation, (2) a discrete wavelet transform block for multi-scale frequency decomposition, and (3) a spatial detail enhancement block for progressive texture recovery. Experimental results on the restoration of BF top IR hazy images demonstrate that the proposed method achieves superior performance with peak signal-to-noise ratio (PSNR) of 25.40 dB and structural similarity index measure (SSIM) of 0.831, surpassing the current state-of-the-art methods (PSNR 24.77 dB, SSIM 0.816) on standard evaluation metrics.
Xu et al. (Tue,) studied this question.