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Learning Degradation-Independent Representations for Camera ISP Pipelines

Yanhui Guo, Fangzhou Luo, Xiaolin Wu

TL;DR

This work tackles degradations in camera ISP pipelines by learning degradation-independent representations (DiR) that generalize to unseen degradations. It introduces DiRNet to extract a shared degradation-free latent via multi-view mutual information maximization and learns a degradation-free reference (DfR) from high-quality images. An alignment network refines the baseline DiR $r^{(0)}$ to a task-ready $r^{+}$ guided by a pilot representation $r^{\rightarrow}$ derived from degraded inputs, enabling joint optimization with downstream tasks. The approach demonstrates strong generalization and improved performance in image restoration, object detection, and instance segmentation across synthetic and real-world ISP degradations, highlighting practical impact for robust machine perception in real cameras.

Abstract

Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images. However, ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises, demosaicing noises, compression artifacts, and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense, these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations, some of which are even unknown, pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues, we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently, it outperforms state-of-the-art methods across various downstream tasks, including blind image restoration, object detection, and instance segmentation, as verified in our experiments.

Learning Degradation-Independent Representations for Camera ISP Pipelines

TL;DR

This work tackles degradations in camera ISP pipelines by learning degradation-independent representations (DiR) that generalize to unseen degradations. It introduces DiRNet to extract a shared degradation-free latent via multi-view mutual information maximization and learns a degradation-free reference (DfR) from high-quality images. An alignment network refines the baseline DiR to a task-ready guided by a pilot representation derived from degraded inputs, enabling joint optimization with downstream tasks. The approach demonstrates strong generalization and improved performance in image restoration, object detection, and instance segmentation across synthetic and real-world ISP degradations, highlighting practical impact for robust machine perception in real cameras.

Abstract

Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images. However, ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises, demosaicing noises, compression artifacts, and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense, these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations, some of which are even unknown, pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues, we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently, it outperforms state-of-the-art methods across various downstream tasks, including blind image restoration, object detection, and instance segmentation, as verified in our experiments.
Paper Structure (24 sections, 2 theorems, 7 equations, 7 figures, 5 tables, 1 algorithm)

This paper contains 24 sections, 2 theorems, 7 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Degradation blind image restoration network
  4. End-to-end neural network solutions for ISP pipelines
  5. Image enhancement for high-level tasks
  6. Approach
  7. Optimizing DiRNet by MMI Maximization
  8. Mutual information maximization
  9. Multi-view mutual information maximization
  10. Degradation-Free Representation Reference
  11. Alignment Network for Refined DiR
  12. Alignment network
  13. Network structure
  14. Discussion on the pilot DfR $\mathop{\mathbf{r}}\limits^{\rightarrow}$
  15. Joint Task-Aligned Refinement Learning
  16. ...and 9 more sections

Key Result

Proposition 1

Let $\mathbf{x}$ and $\mathbf{y}$ represent two random variables, the $\mathbf{J} = p(\mathbf{x},\mathbf{y})$ and $\mathbf{M}=p(\mathbf{x})p(\mathbf{y})$ are the joint and the product of marginals of the two variables, respectively. The mutual information between the variables satisfiesThe derivatio

Figures (7)

  • Figure 1: Comparison of the canonical learning paradigm and our DiR learning technique.
  • Figure 2: Self-supervised MMI maximization for DiRNet.
  • Figure 3: Illustration of the alignment network.
  • Figure 4: Visualization of the pilot DfR representations $\mathop{\mathbf{r}}\limits^{\rightarrow}$ and the expected DfR representations $\mathbf{r}^{\ast}$. The information provided by the pilot DfR can guide the alignment to find the optimal DfR.
  • Figure 5: Illustration of the joint task-aligned refinement learning.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2