  Image light source transfer (LLST), as the most challenging task in the
domain of image relighting, has attracted extensive attention in recent years.
In the latest research, LLST is decomposed three sub-tasks: scene reconversion,
shadow estimation, and image re-rendering, which provides a new paradigm for
image relighting. However, many problems for scene reconversion and shadow
estimation tasks, including uncalibrated feature information and poor semantic
information, are still unresolved, thereby resulting in insufficient feature
representation. In this paper, we propose novel down-sampling feature
self-calibrated block (DFSB) and up-sampling feature self-calibrated block
(UFSB) as the basic blocks of feature encoder and decoder to calibrate feature
representation iteratively because the LLST is similar to the recalibration of
image light source. In addition, we fuse the multi-scale features of the
decoder in scene reconversion task to further explore and exploit more semantic
information, thereby providing more accurate primary scene structure for image
re-rendering. Experimental results in the VIDIT dataset show that the proposed
approach significantly improves the performance for LLST.
