论文标题
在没有干净参考的情况下增强和学习DeNoiser
Enhancing and Learning Denoiser without Clean Reference
论文作者
论文摘要
关于基于学习的图像denoising的最新研究已在各种降低降噪任务上取得了有希望的表现。这些深层的DeNoiser中的大多数是在清洁参考的监督下进行的，或者在合成噪声的监督下进行了训练。合成噪声的假设在面对真实照片时会导致泛化。为了解决这个问题，我们通过将降噪任务作为噪声转移任务的特殊情况来提出一种新颖的深层图像降低方法。学习噪声转移使网络能够通过观察损坏的样本来获取脱氧能力。现实世界中的基准测试的结果表明，我们提出的方法在消除现实噪声方面实现了有希望的表现，从而使其成为减少实际降噪问题的潜在解决方案。
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.
