  Lightness adaptation is vital to the success of image processing to avoid
unexpected visual deterioration, which covers multiple aspects, e.g., low-light
image enhancement, image retouching, and inverse tone mapping. Existing methods
typically work well on their trained lightness conditions but perform poorly in
unknown ones due to their limited generalization ability. To address this
limitation, we propose a novel generalized lightness adaptation algorithm that
extends conventional normalization techniques through a channel filtering
design, dubbed Channel Selective Normalization (CSNorm). The proposed CSNorm
purposely normalizes the statistics of lightness-relevant channels and keeps
other channels unchanged, so as to improve feature generalization and
discrimination. To optimize CSNorm, we propose an alternating training strategy
that effectively identifies lightness-relevant channels. The model equipped
with our CSNorm only needs to be trained on one lightness condition and can be
well generalized to unknown lightness conditions. Experimental results on
multiple benchmark datasets demonstrate the effectiveness of CSNorm in
enhancing the generalization ability for the existing lightness adaptation
methods. Code is available at https://github.com/mdyao/CSNorm.
