Perceptual Decoupling With Heterogeneous Auxiliary Tasks for Joint Low-Light Image Enhancement and Deblurring

Capturing images at night are susceptible to inadequate illumination conditions and motion blurring. Given the typical coupling of these two forms of degradation, a pioneer work takes a compact approach of brightening followed by deblurring. However, this sequential approach may compromise informative features and elevate the likelihood of generating unintended artifacts. In this paper, we observe that the co-existing low light and blurs intuitively impair multiple perceptions, making it difficult to produce visually appealing results. To meet these challenges, we propose perceptual decoupling with heterogeneous auxiliary tasks (PDHAT) for joint low-light image enhancement and deblurring. Based on the crucial perceptual properties of the two degradations, we construct two individual auxiliary tasks: coarse preview prediction (CPP) and high-frequency reconstruction (HFR), so that the perception of color, brightness, edges, and details are decoupled into heterogeneous auxiliary tasks to obtain task-specific representations for parallel assisting the main task: joint low-light enhancement and deblurring (LLE-Deblur). Furthermore, we develop dedicated modules to build the network blocks in each branch based on the exclusive properties of each task. Comprehensive experiments are conducted on LOL-Blur and Real-LOL-Blur datasets, showing that our method outperforms existing methods on quantitative metrics and qualitative results.