  Recent studies on learning-based image denoising have achieved promising
performance on various noise reduction tasks. Most of these deep denoisers are
trained either under the supervision of clean references, or unsupervised on
synthetic noise. The assumption with the synthetic noise leads to poor
generalization when facing real photographs. To address this issue, we propose
a novel deep image-denoising method by regarding the noise reduction task as a
special case of the noise transference task. Learning noise transference
enables the network to acquire the denoising ability by observing the corrupted
samples. The results on real-world denoising benchmarks demonstrate that our
proposed method achieves promising performance on removing realistic noises,
making it a potential solution to practical noise reduction problems.
