컨볼루션 기반 이미지 복원에서 환각 현상 정량화와 신뢰성 확보를 위한 컨포멀 환각 추정 지표
📝 Abstract
U-Net and other U-shaped architectures have achieved significant success in image deconvolution tasks. However, challenges have emerged, as these methods might generate unrealistic artifacts or hallucinations, which can interfere with analysis in safety-critical scenarios. This paper introduces a novel approach for quantifying and comprehending hallucination artifacts to ensure trustworthy computer vision models. Our method, termed the Conformal Hallucination Estimation Metric (CHEM), is applicable to any image reconstruction model, enabling efficient identification and quantification of hallucination artifacts. It offers two key advantages: it leverages wavelet and shearlet representations to efficiently extract hallucinations of image features and uses conformalized quantile regression to assess hallucination levels in a distribution-free manner. Furthermore, from an approximation theoretical perspective, we explore the reasons why U-shaped networks are prone to hallucinations. We test the proposed approach on the CANDELS astronomical image dataset with models such as U-Net, Swin-UNet, and Learnlets, and provide new perspectives on hallucination from different aspects in deep learning-based image processing.
💡 Analysis
U-Net and other U-shaped architectures have achieved significant success in image deconvolution tasks. However, challenges have emerged, as these methods might generate unrealistic artifacts or hallucinations, which can interfere with analysis in safety-critical scenarios. This paper introduces a novel approach for quantifying and comprehending hallucination artifacts to ensure trustworthy computer vision models. Our method, termed the Conformal Hallucination Estimation Metric (CHEM), is applicable to any image reconstruction model, enabling efficient identification and quantification of hallucination artifacts. It offers two key advantages: it leverages wavelet and shearlet representations to efficiently extract hallucinations of image features and uses conformalized quantile regression to assess hallucination levels in a distribution-free manner. Furthermore, from an approximation theoretical perspective, we explore the reasons why U-shaped networks are prone to hallucinations. We test the proposed approach on the CANDELS astronomical image dataset with models such as U-Net, Swin-UNet, and Learnlets, and provide new perspectives on hallucination from different aspects in deep learning-based image processing.
📄 Content
In recent decades, artificial intelligence has permeated nearly every domain, including medical diagnosis pipelines [29], autonomous driving [62], natural language processing Figure 1. An example of hallucinations in astronomical image deconvolution obtained from U-Net predictions. [21], and speech recognition [41], among others. Despite these advances, hallucinations have emerged as a recurring challenge accompanying the success of deep learning. The phenomenon of hallucination has been observed in a wide range of deep learning models, including Large Language Models [61], Large Vision-Language Models [36], Natural Language Generation systems [26], and Large Foundation Models [50].
Specifically, the hallucination phenomenon has been observed in image processing [17], raising concerns in medical imaging [8,58] and in the natural sciences [2,7]. Figure 1 illustrates that hallucinations appear in astronomical image deconvolution tasks [2]. In informal terms, hallucinations refer to realistic textures in predictions that are missing from the ground-truth images. Despite recent efforts to formalize hallucinations in imaging tasks [8,58], there is no consensus on a common framework to quantify hallucinations. In addition to measuring hallucinations, another key challenge is to identify the fundamental issue that leads to these hallucinations. Both understanding and estimating the confidence of the model in its outputs are crucial, as unreliable predictions can lead to misdiagnosis or erroneous decision-making [12]. Given that textures often play a more significant and noticeable role than individual pixels, a hallucination detection algorithm should be designed to be sensitive to artifacts in textures.
This paper focuses on providing a comprehensive analysis to assess and understand hallucination in image processing tasks. We begin by introducing a novel texturelevel hallucination detection algorithm for image processing. To successfully capture texture hallucinations, we use wavelets and shearlets to extract directional detail from images. Given the significance of U-Net and its variations in image processing tasks, we then analyze the reasons why U-shaped architectures tend to produce hallucinations, utilizing approximation theory. Finally, we conduct numerical experiments on the CANDELS dataset [18,27] using U-Net, SwinUNet, and Learnlets. This application represents a particularly important case: while technological advances are producing unprecedented amounts of data, it is equally crucial that reconstruction methods remain reliable, as such reconstruction tasks are safety-critical, where spurious details can lead to misleading interpretations.
Our contributions are outlined as follows: • Hallucination quantification: We introduce a novel hallucination detection approach: Conformal Hallucination Estimation Metric (CHEM). Our method is designed to efficiently capture implausible but realistic textures by wavelets/shearlets, and we do not require prior knowledge on the underlying distributions of images, which makes our algorithm efficient. • Critical factors behind hallucinations: Based on approximation theory, we offer a comprehensive analysis of the reasons behind hallucinations in deep learning methods.
The key factors include the model’s limited parameters and the properties of input images. Our result also characterizes the expressivity of U-shaped networks for general image processing tasks. • Experimental Results: We analyze the impact of different dictionaries (wavelets, shearlets), loss functions (ℓ 1 , ℓ 2 ), and training epochs. Two main findings emerged: (i) there is a tradeoff between accuracy and hallucination, and (ii) models with similar accuracy exhibit different robustness to input perturbations. This work offers new insights into the evaluation of deep learning models in imaging and potentially paves the way to new methodologies to design deep learning techniques or reduce model hallucination in image processing. The source code for the experiments will be made publicly available.
Hallucinations in imaging: Deep learning-based approaches in image processing tasks have been reported to introduce undesired effects. The work [12] established a fundamental trade-off in generative image restoration: improving perceptual quality inevitably increases uncertainty. In medical imaging, hallucinations have been identified as a major concern in integrating deep learning into the image reconstruction pipeline [8,44]. To understand the underlying mechanisms responsible for hallucinations, theoretical foundations were developed in [17], revealing their prevalence in deep learning models. The study also indicated that achieving an optimal image reconstruction method during training might be impossible. Hallucination measurements: While hallucinations in image processing have been documented, methods for their assessment remain limited. We refer the reader to the following works for a detailed overvie
This content is AI-processed based on ArXiv data.