Image denoising is a classic restoration problem. Yet, current deep learning methods are subject to the problems of generalization and interpretability. To mitigate these problems, in this project, we present a framework that is capable of controllable, confidence-based noise removal. The framework is based on the fusion between two different denoised images, both derived from the same noisy input. One of the two is denoised using generic algorithms (e.g. Gaussian), which make few assumptions on the input images, therefore, generalize in all scenarios. The other is denoised using deep learning, performing well on seen datasets. We introduce a set of techniques to fuse the two components smoothly in the frequency domain. Beyond that, we estimate the confidence of a deep learning denoiser to allow users to interpret the output, and provide a fusion strategy that safeguards them against out-of-distribution inputs. Through experiments, we demonstrate the effectiveness of the proposed framework in different use cases.
翻译:图像脱色是一个典型的修复问题。 然而, 目前深层的学习方法会遇到一般化和可解释的问题。 为了缓解这些问题, 我们在此项目中提出了一个能够控制、 信任的噪音清除框架。 这个框架基于两种不同的脱色图像的融合, 两种图像来自相同的噪音输入。 其中一种是使用通用算法( 例如高森) 来取消对输入图像的假设, 因此, 在所有情况下都普遍化。 另一种是使用深层学习来取消命名, 在可见的数据集上表现良好。 我们引入了一套技术来顺利地融合频率领域的两个组成部分。 除此之外, 我们估计了深层的脱色图像的信心, 让用户解释输出, 并提供一种保护用户不受分配外输入的融合战略。 通过实验, 我们展示了在不同使用案例中拟议框架的有效性 。