When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

Abstract: Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning. The code is available online https://github.com/Ding-Liu/DeepDenoising.

• The paper introduces a dual-faceted network design that couples image denoising with high-level vision tasks to improve overall performance.
• It employs a joint loss mechanism to simultaneously optimize image reconstruction and semantic accuracy without task-specific retraining.
• Experimental results demonstrate significant improvements in PSNR and IoU, enhancing classification and segmentation on noisy images.

Integrating Image Denoising with High-Level Vision Tasks via Deep Learning

This paper presents an investigation into the interaction between image denoising and high-level vision tasks, highlighting a novel deep learning approach that effectively combines these traditionally independent domains. The proposed methodology integrates a convolutional neural network for image denoising with a deep neural network tasked with high-level vision functionalities, such as image classification and semantic segmentation. This integrated approach is validated through rigorous experimentation, demonstrating its efficacy in both enhancing denoising quality and improving the performance of high-level vision tasks on noisy images.

Key Contributions

1. Dual-Faceted Network Design: The authors introduce a CNN architecture for image denoising that achieves state-of-the-art performance benchmarks. This network is then coupled with high-level vision networks through a cascaded architecture. By incorporating a joint loss mechanism, the denoising process is directly influenced by high-level semantic information, leading to enhanced visual output and increased task accuracy.
2. Joint Loss Mechanism: A pivotal aspect of this work is the use of a combined loss function that optimizes both image reconstruction and task-specific vision performance. This joint optimization enables the learning of a denoiser that not only performs effectively in enhancing image quality but also maintains robustness across different high-level vision tasks without the need for task-specific retraining.
3. Generality and Robustness: The proposed framework exhibits the capacity for a single denoising module to be effectively utilized across different high-level tasks, demonstrating the generality of semantic information extracted during training. This aspect significantly reduces the complexity and resource requirements typically associated with individual task-specific training regimes.

Experimental Validation

The paper's experimental section provides substantial evidence supporting the proposed approach. Quantitative measures, such as Peak Signal-to-Noise Ratio (PSNR) for denoising tasks and mean Intersection-over-Union (IoU) for semantic segmentation, highlight significant improvements over conventional methods. The results indicate that incorporating high-level semantics in the denoising process reduces artifacts associated with traditional methods, ultimately leading to more visually appealing reconstructions.

Specific experiments demonstrate that denoising networks trained with semantic guidance boost performance in high-level tasks evaluated on the ILSVRC2012 and Pascal VOC 2012 datasets. Notably, the cascaded networks outperform existing benchmarks in both top-1 and top-5 accuracy for classification and demonstrate superior segmentation results on noisy images.

Implications and Future Directions

The implications of these findings extend beyond the immediate quantitative improvements. Practically, this approach facilitates real-time applications by integrating preprocessing and high-level semantic extraction into a singular model, optimizing computational resources. Theoretically, this work elucidates the potential synergies between low-level and high-level vision processing, encouraging further exploration into holistic vision models that transcend traditional task boundaries.

Future research can explore extending the principles demonstrated here to address other forms of image degradation or corruption. Additionally, investigating the application of this framework in real-world scenarios, such as autonomous driving or medical imaging, may yield further insights into its potential versatility and impact.

In conclusion, this paper establishes a valuable intersection between foundational image processing and advanced vision tasks, providing a significant contribution to the ongoing evolution of computer vision methodologies.