Camera Image Signal Processing (ISP) pipelines can get appealing results in different image signal processing tasks. Nonetheless, the majority of these methods, including those employing an encoder-decoder deep architecture for the task, typically utilize a uniform filter applied consistently across the entire image. However, it is natural to view a camera image as heterogeneous, as the color intensity and the artificial noise are distributed vastly differently, even across the two-dimensional domain of a single image. Varied Moire ringing, motion blur, color-bleaching, or lens-based projection distortions can all potentially lead to a heterogeneous image artifact filtering problem. In this paper, we present a specific patch-based, local subspace deep neural network that improves Camera ISP to be robust to heterogeneous artifacts (especially image denoising). We call our three-fold deep-trained model the Patch Subspace Learning Autoencoder (PSL-AE). The PSL-AE model does not make assumptions regarding uniform levels of image distortion. Instead, it first encodes patches extracted from noisy a nd clean image pairs, with different artifact types or distortion levels, by contrastive learning. Then, the patches of each image are encoded into corresponding soft clusters within their suitable latent sub-space, utilizing a prior mixture model. Furthermore, the decoders undergo training in an unsupervised manner, specifically trained for the image patches present in each cluster. The experiments highlight the adaptability and efficacy through enhanced heterogeneous filtering, both from synthesized artifacts but also realistic SIDD image pairs.

翻译：相机图像信号处理（ISP）流水线能够在不同的图像信号处理任务中取得令人满意的结果。然而，包括采用编码器-解码器深度架构的任务方法在内的大多数方法，通常对整个图像一致地应用同一种均匀滤波器。实际上，由于颜色强度和人工噪声在单张图像的二维域内分布差异很大，将相机图像视为异质性是自然而然的。各种莫尔波纹、运动模糊、颜色漂白或基于镜头的投影畸变都可能导致异质图像伪影过滤问题。本文提出了一种特定的基于补片、局部子空间的深度神经网络，使相机ISP对异质伪影（特别是图像去噪）具有鲁棒性。我们将这一三重深度训练模型称为补片子空间学习自编码器（PSL-AE）。PSL-AE模型不对图像畸变的均匀程度做出假设，而是首先通过对比学习，从具有不同伪影类型或畸变水平的含噪和干净图像对中提取补片。然后，利用先验混合模型，将每张图像的补片编码到其合适的潜在子空间中的对应软簇中。此外，解码器以无监督方式进行训练，专门针对每个簇中的图像补片进行训练。实验通过增强的异质滤波效果（包括合成伪影和真实SIDD图像对）凸显了该方法的适应性和有效性。