Wavelet-based Approach for the Fusion of Low-light Image Pairs

When taking pictures in low-light scene, due to the insufficient light, we are often posed to the following problem: Using short exposure setting, image tends to be dim and noise, but with a sharp outline. While using longer exposure setting, image captures more color and detail information, but with partly blurred areas. A very common situation, none of those images is good enough. Good brightness and color information are retained in long-exposure images, while sharp outlines are retained in shorter ones. In this paper, we propose a fusion method based on wavelet decomposition for such low-light image pair. In this work, we firstly decompose the original image pair into different frequency subbands. After that, we compute the importance weight maps according to the difference value between corresponding subbands. In order to refuse artifacts and ghost, we compute weight maps in Gauss model. Finally, the coefficients of subbands are blended into a high-quality fusion image. Experimental results show that the proposed method effectively preserves sharp edges of the short-exposure image, and maintains the color, brightness, and details of the long-exposure image.