  When taking photos under an environment with insufficient light, the exposure
time and the sensor gain usually require to be carefully chosen to obtain
images with satisfying visual quality. For example, the images with high ISO
usually have inescapable noise, while the long-exposure ones may be blurry due
to camera shake or object motion. Existing solutions generally suggest to seek
a balance between noise and blur, and learn denoising or deblurring models
under either full- or self-supervision. However, the real-world training pairs
are difficult to collect, and the self-supervised methods merely rely on blurry
or noisy images are limited in performance. In this work, we tackle this
problem by jointly leveraging the short-exposure noisy image and the
long-exposure blurry image for better image restoration. Such setting is
practically feasible due to that short-exposure and long-exposure images can be
either acquired by two individual cameras or synthesized by a long burst of
images. Moreover, the short-exposure images are hardly blurry, and the
long-exposure ones have negligible noise. Their complementarity makes it
feasible to learn restoration model in a self-supervised manner. Specifically,
the noisy images can be used as the supervision information for deblurring,
while the sharp areas in the blurry images can be utilized as the auxiliary
supervision information for self-supervised denoising. By learning in a
collaborative manner, the deblurring and denoising tasks in our method can
benefit each other. Experiments on synthetic and real-world images show the
effectiveness and practicality of the proposed method. Codes are available at
https://github.com/cszhilu1998/SelfIR.
