Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline (1905.02538v3)

Abstract: Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that applies demosaicing (DM), denoising (DN), and super-resolution (SR) sequentially in a fixed and predefined pipeline (execution order of tasks), DM$\to$DN$\to$SR. The most recent work on image processing focuses on developing more sophisticated architectures to achieve higher image quality. Little attention has been paid to the design of the pipeline, and it is still not clear how significant the pipeline is to image quality. In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions. On the one hand, in sequential solutions, we find that the pipeline has a non-trivial effect on the resulted image quality. Our suggested pipeline DN$\to$SR$\to$DM yields consistently better performance than other sequential pipelines in various experimental settings and benchmarks. On the other hand, in joint solutions, we propose an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves state-of-the-art performance for the mixture problem. We further present a novel and simple method that can integrate a certain pipeline into a given end-to-end network by providing intermediate supervision using a detachable head. Extensive experiments show that an end-to-end network with the proposed pipeline can attain only a consistent but insignificant improvement. Our work indicates that the investigation of pipelines is applicable in sequential solutions, but is not very necessary in end-to-end networks. \RR{Code, models, and our contributed PixelShift200 dataset are available at \url{https://github.com/guochengqian/TENet}

• The paper proposes a new sequential pipeline order (DN→SR→DM) that improves PSNR by about 0.28 dB over traditional methods.
• It introduces the TENet architecture, an end-to-end network that integrates demosaicing, denoising, and super-resolution with intermediate supervision.
• The study also presents the PixelShift200 dataset, enabling artifact-free training and benchmarking to optimize processing pipelines.

An In-depth Review of "Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline"

The paper "Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline" provides a comprehensive investigation into the impact of pipeline ordering on image quality tasks such as demosaicing (DM), denoising (DN), and super-resolution (SR). The authors propose a novel pipeline order and present insights into how these processes interact, both individually and jointly, within a learning-based framework.

Key Contributions

1. Pipeline Order for Sequential Solutions: The paper reveals that the traditional sequential pipeline of DM→DN→SR is suboptimal. The authors propose a novel order: DN→SR→DM. This ordering consistently yields better performance in various settings, particularly mitigating artifacts introduced by task interactions.
2. End-to-end Network Design: For joint solutions, the research presents the Trinity Pixel Enhancement NETwork (TENet), which achieves state-of-the-art performance for the combined demosaicing, denoising, and super-resolution tasks. This network architecture is distinctive for its ability to integrate pipelines via intermediate supervision with a detachable module, albeit yielding only marginal improvements.
3. Pipeline's Necessity in End-to-end Networks: Extensive experiments indicate that while the pipeline configuration is vital for sequential solutions, it contributes insignificantly to joint solutions. This suggests that the specific task order in an end-to-end approach does not substantially affect performance.
4. Dataset Contribution: The authors introduce PixelShift200, a new dataset with full-color samples which aids in better training and benchmarking image processing tasks, free of artifacts typically introduced during color interpolation.

Numerical Impact

Notably, the proposed DN→SR→DM pipeline improves PSNR by approximately 0.28 dB over other sequential pipelines. In terms of joint architectures, the TENet outperforms the vanilla approach with a PSNR gain, though the margin is less than 1 dB, indicating the relatively limited benefit of pipeline reordering in joint tasks when using deep networks.

Practical and Theoretical Implications

Practically, the insights from this paper could reshape how learning-based image processing systems are architected, particularly in mobile and embedded systems where resource constraints necessitate optimal operation order. Theoretically, the findings suggest an area of diminished returns in joint processing pipelines where components do not interact poorly, highlighting potential areas for architectural improvements beyond task order.

Future Directions

Future research could explore dynamic pipeline formations that adapt during inference or cross-component optimizations that focus on shared feature learning rather than task order alone. Additionally, expanding the scope to multi-frame processing scenarios could offer new insights for practical deployment in real-world applications.

Overall, this paper contributes valuable insights by questioning established practices and pushing forward the understanding of how to efficiently solve complex image processing tasks through both pipeline optimization and end-to-end learning approaches.