Low-Light Monocular Depth Estimation Algorithm Based on Illumination Adaptive Image Enhancement

Depth estimation in low-light scenes is an enormous challenge in the field of monocular depth estimation. Although numerous algorithms have attempted to improve their performance in low-light scenarios through a variety of techniques, the inconsistent illumination issue caused by local intense or colored light is rarely taken into consideration. To tackle this problem, we proposed an illumination adaptive image enhancement-based low-light depth estimation algorithm (IAIE_LDE) in this paper. Our main contribution is an S-shaped illumination estimation basis illumination adaptive consistent correction model, which is designed to eliminate the edge blurring and depth hole effects in depth maps caused by inconsistent lighting. Meanwhile, a low-light depth estimation architecture consisting of three modules, namely, illumination adaptive correction, low-light image enhancement and depth estimation modules, is constructed and trained. Specifically, the first sub-module is designed to alleviate the illumination inconsistency utilizing the proposed S-shaped illumination adaptive correction model by calculating the corresponding correction coefficients for each pixel according to the estimated illumination; the core module of the classic EnlightGAN algorithm is adopted in the second sub-module to improve the overall brightness of the image and solve the other problems caused by low light; the ZoeDepth model is chosen as our depth estimation sub-module to output a depth map comparable to high-quality illuminated images. Extensive experiments on the widely used Oxford RobotCar and nuScenes datasets indicate superior performance of our method by comparing it with state-of-the-art low-light depth estimation algorithms such as RNW, STEPS, ADDS-DepthNet, and ACDepth, both qualitatively and quantitatively.