Efficient Infrared Image Super-Resolution Reconstruction via Guided Filter Coefficients Estimation with Parallax Attention Mechanism

Abstract

Due to the spectral range mismatch between the images, building an efficient infrared (IR) image super-resolution algorithm suitable for embedded devices remains a significant challenge. Given that visible images possess more abundant high-frequency information compared to infrared images, we utilize the visible light to guide infrared image super-resolution reconstruction. Specifically, we transfer the reconstruction task to a guided filter learning process, whose coefficients are estimated by joint learning of visible and infrared image to complete the reconstruction through homologous constraints. In order to efficiently predict guided filter coefficients, we design a lightweight network which incorporates reparameterized differential convolution blocks and a feature fusion strategy. Striving to enhance the fusion strategy performance, we utilize parallax attention mechanism to solve the non-pixel registration problem between infrared and visible images. Extensive experiments on two challenging IR image datasets show that our method performs SOTA in terms of PSNR, SSIM and LPIPS as compared to current state-of-the-art approaches while showing its effectiveness and practicality in the edge platform of RK3588.