Examining the Impact of Blur on Recognition by Convolutional Networks (1611.05760v2)

Abstract: State-of-the-art algorithms for many semantic visual tasks are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annotated datasets of artifact-free high-quality images. In this paper, we investigate the effect of one such artifact that is quite common in natural capture settings: optical blur. We show that standard network models, trained only on high-quality images, suffer a significant degradation in performance when applied to those degraded by blur due to defocus, or subject or camera motion. We investigate the extent to which this degradation is due to the mismatch between training and input image statistics. Specifically, we find that fine-tuning a pre-trained model with blurred images added to the training set allows it to regain much of the lost accuracy. We also show that there is a fair amount of generalization between different degrees and types of blur, which implies that a single network model can be used robustly for recognition when the nature of the blur in the input is unknown. We find that this robustness arises as a result of these models learning to generate blur invariant representations in their hidden layers. Our findings provide useful insights towards developing vision systems that can perform reliably on real world images affected by blur.

• The paper establishes that Convolutional Neural Networks (CNNs), trained on sharp images, suffer significant performance degradation when presented with blurred inputs, quantifying this impact on recognition accuracy.
• Fine-tuning pre-trained CNNs with a mixture of sharp and blurred images effectively recovers lost accuracy and achieves robustness against various blur types, proving more efficient than deblurring before recognition.
• The research reveals that CNNs can develop blur-invariant feature representations in their deeper layers, demonstrating the networks' capacity to learn adaptive features when trained on appropriately diverse data.

The Impact of Blur on Recognition by Convolutional Networks

This paper presents a focused paper on how blur affects the recognition capabilities of convolutional neural networks (CNNs), which are prevalent in various computer vision tasks such as image classification, object detection, and semantic segmentation. The authors investigate this phenomenon within the context of neural networks trained and evaluated on large, annotated datasets composed predominantly of high-quality and artifact-free images.

Key Findings

1. Performance Degradation Due to Blur: The work establishes that CNNs, trained solely on sharp images, experience a marked decline in recognition performance when subjected to blurred inputs. This degradation occurs across various types and degrees of blur, including defocus, subject motion, and camera motion blur. The paper quantifies this impact with numerical assessments of top-5 accuracy on blurred versions of standard datasets.
2. Mitigation via Fine-tuning: Through experiments, the authors demonstrate that fine-tuning pre-trained networks with a mixture of sharp and blurred images significantly recovers the lost accuracy. For instance, a network fine-tuned with multiple types of blur achieves robustness against unknown blurs without sacrificing performance on sharp images. This result highlights the adaptability of CNNs when exposed to new data distributions.
3. Blur Invariance in Hidden Representations: The research explores the internal mechanisms of CNNs to understand the adaptation process. It is found that networks can formulate blur-invariant feature representations in their deeper layers. This finding suggests that the intricate architectures of CNNs are capable of learning invariant features given appropriate training data.
4. Generality and Cross-blur Resilience: Experiments reveal a substantial degree of generalization across different blur types. A model fine-tuned with a diverse set of blurs performs solidly even when evaluated on unseen blur types, underscoring the potential for generalized robustness in CNNs.
5. Comparison with Deblurring: The authors also discuss the alternative approach of deblurring images before recognition. They find that fine-tuning with blurred images is more computationally efficient and equally, if not more, effective than explicit deblurring followed by recognition with models trained on sharp images.

Theoretical and Practical Implications

This research holds significant implications for both theory and practice in computer vision. From a theoretical standpoint, it underscores the plasticity of CNNs and their potential for acquiring adaptive capabilities beyond initial training conditions. Practically, the findings inform the design of real-world vision systems that must handle images afflicted by various artifacts, such as blur.

The provided insights are particularly relevant for deploying vision systems in environments where image quality cannot be guaranteed, such as in robotics, autonomous vehicles, and surveillance systems. Moreover, this paper lays a foundation for further exploration into other image artifacts, advocating a shift towards training paradigms that foster robustness to a broader spectrum of image degradations.

Speculations on Future Directions

The notion of achieving robustness via fine-tuning suggests intriguing possibilities for unsupervised or self-supervised learning techniques. Future investigations might explore whether CNNs can learn to adapt to new distributions of degradation without explicit retraining. Moreover, understanding how other modalities of image degradation, such as noise or compression artifacts, impact CNN performance remains an open and vital research area.

In conclusion, this paper provides a compelling examination of the effects of blur on CNN recognition performance and presents a practical solution for enhancing robustness. Its contributions have the potential to refine how neural networks are trained and deployed, extending their applicability in non-ideal and unpredictable real-world conditions.