  Despite the recent progress in enhancing the efficacy of image deblurring,
the limited decoding capability constrains the upper limit of State-Of-The-Art
(SOTA) methods. This paper proposes a pioneering work, Adaptive Patch Exiting
Reversible Decoder (AdaRevD), to explore their insufficient decoding
capability. By inheriting the weights of the well-trained encoder, we refactor
a reversible decoder which scales up the single-decoder training to
multi-decoder training while remaining GPU memory-friendly. Meanwhile, we show
that our reversible structure gradually disentangles high-level degradation
degree and low-level blur pattern (residual of the blur image and its sharp
counterpart) from compact degradation representation. Besides, due to the
spatially-variant motion blur kernels, different blur patches have various
deblurring difficulties. We further introduce a classifier to learn the
degradation degree of image patches, enabling them to exit at different
sub-decoders for speedup. Experiments show that our AdaRevD pushes the limit of
image deblurring, e.g., achieving 34.60 dB in PSNR on GoPro dataset.
