  Image restoration algorithms such as super resolution (SR) are indispensable
pre-processing modules for object detection in degraded images. However, most
of these algorithms assume the degradation is fixed and known a priori. When
the real degradation is unknown or differs from assumption, both the
pre-processing module and the consequent high-level task such as object
detection would fail. Here, we propose a novel framework, RestoreDet, to detect
objects in degraded low resolution images. RestoreDet utilizes the downsampling
degradation as a kind of transformation for self-supervised signals to explore
the equivariant representation against various resolutions and other
degradation conditions. Specifically, we learn this intrinsic visual structure
by encoding and decoding the degradation transformation from a pair of original
and randomly degraded images. The framework could further take the advantage of
advanced SR architectures with an arbitrary resolution restoring decoder to
reconstruct the original correspondence from the degraded input image. Both the
representation learning and object detection are optimized jointly in an
end-to-end training fashion. RestoreDet is a generic framework that could be
implemented on any mainstream object detection architectures. The extensive
experiment shows that our framework based on CenterNet has achieved superior
performance compared with existing methods when facing variant degradation
situations. Our code would be released soon.
