A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation

• URL: http://arxiv.org/abs/2605.15906v1
• Date: Fri, 15 May 2026 12:44:02 GMT
• Title: A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation
• Abstract summary: We propose a causally grounded framework for organizing and interpreting image degradations.<n>Instead of introducing new degradations or redefining existing benchmarks, we provide an interpretive representation and measurement layer.<n>We demonstrate the framework through COCO Degradation, a taxonomy aligned benchmark for evaluating object detector robustness under diverse imaging conditions.
• Score: 3.8265249634979734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image degradations can occur during acquisition, processing, and transmission, altering visual appearance and affecting downstream vision tasks. They are studied in several communities, including synthetic corruption benchmarks for robustness evaluation, perceptual image quality assessment, and physically grounded analyses of imaging systems or real camera failures. Although these areas address closely related phenomena, they often use incompatible grouping schemes and backend specific severity definitions, making results difficult to compare across datasets, degradation sources, and tasks. We propose a causally grounded framework for organizing and interpreting image degradations across these settings. Instead of introducing new degradations or redefining existing benchmarks, we provide an interpretive representation and measurement layer that makes implicit assumptions explicit. Each degradation is described along two orthogonal axes: its dominant causal source in the imaging pipeline (environment, sensor/optics, ISP/renderer/codec, or transfer/system), and its resulting perceptual effect. This dual axis abstraction yields a compact taxonomy spanning algorithmic corruptions, perceptual distortions, and physically motivated imaging artifacts. To address inconsistent severity semantics without changing existing implementations, we introduce a lightweight severity measurement layer. For every degradation and each native severity level of a given backend, we quantify degradation strength using full reference image quality metrics: PSNR, SSIM, and LPIPS. This makes severity observable and comparable across sources while preserving native parameterizations. We demonstrate the framework through COCO Degradation, a taxonomy aligned benchmark for evaluating object detector robustness under diverse imaging conditions.

Related papers

• Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images [3.5122049834653706]
  Whole-slide images (WSIs) provide rich tissue-level and cellular-level information.<n>Low-magnification pathology images with limited annotations can achieve performance comparable to high-magnification images.
  arXiv  Detail & Related papers  (2026-07-12T14:23:50Z)
• Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification [54.952203312050564]
  Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification.<n>We propose FogFool, a physically plausible adversarial framework that generates fog-based perturbations.<n>Experiments on two benchmark RS datasets demonstrate that FogFool achieves superior performance.
  arXiv  Detail & Related papers  (2026-04-16T05:47:57Z)
• Architectural Unification for Polarimetric Imaging Across Multiple Degradations [54.83037963738918]
  We propose a unified architectural framework for polarimetric imaging that is structurally shared across multiple degradation scenarios.<n>The model performs single-stage joint image-Stokes processing, avoiding error accumulation and explicitly preserving physical consistency.
  arXiv  Detail & Related papers  (2026-03-06T02:38:57Z)
• Self-Aware Object Detection via Degradation Manifolds [3.8265249634979734]
  In safety-critical settings, it is insufficient to produce predictions without assessing whether the input remains within the detector's nominal operating regime.<n>We introduce a degradation-aware self-awareness framework based on degradation manifold.<n>Our method augments a standard detection backbone with a lightweight embedding head trained via contrastive learning.
  arXiv  Detail & Related papers  (2026-02-20T17:58:46Z)
• Hybrid Image Resolution Quality Metric (HIRQM):A Comprehensive Perceptual Image Quality Assessment Framework [0.0]
  We propose the Hybrid Image Resolution Quality Metric (HIRQM) to integrate statistical, multi-scale, and deep learning methods for a comprehensive quality evaluation.<n>A dynamic weighting mechanism adapts component contributions based on image characteristics like brightness and variance, enhancing flexibility across distortion types.<n> evaluated on TID2013 and LIVE datasets, HIRQM Pearson and Spearman correlations of 0.92 and 0.90, outperforming traditional metrics.
  arXiv  Detail & Related papers  (2025-05-04T06:14:10Z)
• PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment [59.9103803198087]
  We propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA)<n>By leveraging underwater radiative transfer theory, we integrate physics-based imaging estimations to establish quantitative metrics for these distortions.<n>The proposed model accurately predicts image quality scores and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-20T03:31:45Z)
• Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints [32.07631215590755]
  Contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion.
  arXiv  Detail & Related papers  (2023-04-25T05:55:28Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.