Recently, discriminative learning methods have gained substantial interest in solving inverse imaging problems due to their decent performance and fast inferencing capability. Those methods need separate models for specific noise levels, which in turn require multiple models to be trained to denoise an image. However, images exhibit spatial variant noise which limits the applicability of such methods. In addition, the discriminative learning methods introduce artifacts such as blurring, deblocking, and so forth while denoising an image. To address these issues, we propose a cascaded and recursive convolutional neural network (CRCNN) framework which can cope with spatial variant noise and blur artifacts in a single denoising framework. The CRCNN takes into account down-sampled sub-images for fast inferencing along with the noise level map. We adopt the hybrid orthogonal projection and estimation method on the convolutional layers to improve the generalization capability of the network in terms of non-uniform and spatial variant noise levels. In contrast to the existing methods, the CRCNN framework allows both denoising and deblurring of images using a single framework which preserves the fine details in a denoised image. Extensive experiments have been conducted to validate the effectiveness and flexibility of the CRCNN framework on real as well as synthetic noisy images in comparison to the state-of-the-art denoising methods. The results show that the CRCNN performs effectively on both synthetic as well as spatial variant noise-induced images, thus, proving the practicability of the framework.

最近，由于其良好的性能和快速的推理能力，判别学习方法在解决逆成像问题方面引起了极大的兴趣。这些方法需要针对特定​​噪声水平的单独模型，而这又需要训练多个模型来对图像进行去噪。然而，图像表现出空间变异噪声，这限制了这种方法的适用性。此外，判别式学习方法在对图像去噪时会引入伪影，例如模糊、去块等。为了解决这些问题，我们提出了一种级联递归卷积神经网络 (CRCNN) 框架，该框架可以在单个去噪框架中处理空间变异噪声和模糊伪影。CRCNN 考虑下采样子图像以进行快速推理以及噪声水平图。我们在卷积层上采用混合正交投影和估计方法，以提高网络在非均匀和空间变异噪声水平方面的泛化能力。与现有方法相比，CRCNN 框架允许使用单个框架对图像进行去噪和去模糊，该框架保留了去噪图像中的精细细节。与最先进的去噪方法相比，已经进行了大量实验以验证 CRCNN 框架在真实和合成噪声图像上的有效性和灵活性。结果表明，CRCNN 在合成图像和空间变体噪声诱导图像上均有效执行，从而证明了该框架的实用性。