Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution. Prevalent discriminative networks trained on synthetic noise models have been shown to generalize poorly to real-world noisy images. While curating real-world noisy images and improving ground truth estimation procedures remain key points of interest, a potential research direction is to explore extensions to the widely used convolutional neuron model to enable better generalization with fewer data and lower network complexity, as opposed to simply using deeper Convolutional Neural Networks (CNNs). Operational Neural Networks (ONNs) and their recent variant, Self-organized ONNs (Self-ONNs), propose to embed enhanced non-linearity into the neuron model and have been shown to outperform CNNs across a variety of regression tasks. However, all such comparisons have been made for compact networks and the efficacy of deploying operational layers as a drop-in replacement for convolutional layers in contemporary deep architectures remains to be seen. In this work, we tackle the real-world blind image denoising problem by employing, for the first time, a deep Self-ONN. Extensive quantitative and qualitative evaluations spanning multiple metrics and four high-resolution real-world noisy image datasets against the state-of-the-art deep CNN network, DnCNN, reveal that deep Self-ONNs consistently achieve superior results with performance gains of up to 1.76dB in PSNR. Furthermore, Self-ONNs with half and even quarter the number of layers that require only a fraction of computational resources as that of DnCNN can still achieve similar or better results compared to the state-of-the-art.

中文翻译：

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卷积与自组织运算神经网络的真实世界盲图像降噪

由于底层噪声分布的不确定性，现实世界中的盲降噪带来了独特的图像恢复挑战。已经证明，在合成噪声模型上训练的普遍判别网络不能很好地推广到现实世界中的噪点图像。尽管策划现实世界中的嘈杂图像和改善地面真相估计程序仍然是关注的重点，但潜在的研究方向是探索对广泛使用的卷积神经元模型的扩展，从而以更少的数据和更低的网络复杂度实现更好的概括，而不仅仅是简单使用更深的卷积神经网络（CNN）。运作神经网络（ONN）及其最新变体，自组织ONN（Self-ONN），提出将增强的非线性嵌入神经元模型，并在各种回归任务中表现出优于CNN的性能。但是，所有这些比较都是针对紧凑型网络进行的，部署操作层作为卷积层在当代深度架构中的替代品的功效仍有待观察。在这项工作中，我们首次采用了深度的Self-ONN来解决现实世界中的盲像降噪问题。针对最新的深层CNN网络DnCNN，对多个指标和四个高分辨率真实世界的噪点图像数据集进行了广泛的定量和定性评估，结果表明，深层的Self-ONN始终可实现出色的结果，性能提升高达PSNR为1.76dB。此外，