High Dynamic Range Image Reconstruction Based on Dual-Attention Network

The existing deep-learning-based high dynamic range (HDR) image reconstruction methods used for HDR image reconstruction are prone to losing detailed information and providing poor color saturation. This is because the input image is overexposed or underexposed. To address this issue, we propose a dual-attention network-based HDR image reconstruction method. First, this method utilizes the dual-attention module (DAM) to apply the attention mechanism from pixel and channel dimensions, respectively, to extract and fuse the features of two overexposed or underexposed source images, and obtain a preliminary fusion image. Next, a feature enhancement module (FEM) is constructed to perform detail enhancement and color correction for the fused images. The final reference to contrastive learning is generating images closer to the reference image and away from the source image. After multiple trainings, the HDR image is finally generated. The experimental results show that our proposed method achieves the best evaluation results on peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and learned perceptual image patch similarity (LPIPS). Moreover, the generated HDR image exhibits good color saturation and accurate details.