  With the emergence of mobile devices, there is a growing demand for an
efficient model to restore any degraded image for better perceptual quality.
However, existing models often require specific learning modules tailored for
each degradation, resulting in complex architectures and high computation
costs. Different from previous work, in this paper, we propose a unified manner
to achieve joint embedding by leveraging the inherent similarities across
various degradations for efficient and comprehensive restoration. Specifically,
we first dig into the sub-latent space of each input to analyze the key
components and reweight their contributions in a gated manner. The intrinsic
awareness is further integrated with contextualized attention in an X-shaped
scheme, maximizing local-global intertwining. Extensive comparison on
benchmarking all-in-one restoration setting validates our efficiency and
effectiveness, i.e., our network sets new SOTA records while reducing model
complexity by approximately -82% in trainable parameters and -85\% in FLOPs.
Our code will be made publicly available
at:https://github.com/Amazingren/AnyIR.
