DeLECA: Deblurring for Long and short Exposure images with a dual-branch multimodal cross attention mechanism

Blurred images often result from camera shake or object motion, complicating the visual inspection and recognition of objects. To address this issue, we propose DeLECA, a dual-branch Transformer architecture that leverages the complementary nature of the paired blurred images, obtained with long-exposure times, and noisy images, captured with short exposure times, to improve the quality and sharpness of the blurred images. We evaluate DeLECA using two public datasets, GoPro and HIDE. Experimental results show that DeLECA outperforms existing methods, achieving PSNR of 36.08 dB and SSIM of 0.965 on the GoPro dataset, and 40.05 dB and 0.972 on the HIDE dataset.