  Blind image deblurring (BID) remains a challenging and significant task.
Benefiting from the strong fitting ability of deep learning, paired data-driven
supervised BID method has obtained great progress. However, paired data are
usually synthesized by hand, and the realistic blurs are more complex than
synthetic ones, which makes the supervised methods inept at modeling realistic
blurs and hinders their real-world applications. As such, unsupervised deep BID
method without paired data offers certain advantages, but current methods still
suffer from some drawbacks, e.g., bulky model size, long inference time, and
strict image resolution and domain requirements. In this paper, we propose a
lightweight and real-time unsupervised BID baseline, termed Frequency-domain
Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with
attractive properties, i.e., no image domain limitation, no image resolution
limitation, 25x lighter than SOTA, and 5x faster than SOTA. To guarantee the
lightweight property and performance superiority, two new collaboration units
called lightweight domain conversion unit(LDCU) and parameter-free
frequency-domain contrastive unit(PFCU) are designed. LDCU mainly implements
inter-domain conversion in lightweight manner. PFCU further explores the
similarity measure, external difference and internal connection between the
blurred domain and sharp domain images in frequency domain, without involving
extra parameters. Extensive experiments on several image datasets demonstrate
the effectiveness of our FCL-GAN in terms of performance, model size and
reference time.
