Interpretable Image Processing Modules

• Interpretable image processing modules are algorithmic components that expose internal decision processes via prototype matching, algorithm unrolling, and attention mechanisms.
• They enable transparent tracing from pixel-level features to final predictions, ensuring robust auditability in safety-critical applications.
• Recent advances leverage CNNs, ViTs, fuzzy logic, and modular pipelines to achieve high prediction accuracy without sacrificing interpretability.

Interpretable image processing modules are algorithmic or neural network components explicitly designed to expose internal decision processes, enabling users to quantitatively or qualitatively trace, audit, or visualize how derived image representations inform downstream predictions. Such modules enforce or exploit interpretable parameterizations—including prototype matching, explicit algorithm unrolling, feature masking, and attention mechanisms—to provide robust explanations of model behavior at both global and local (e.g., pixel or region) levels. Modern developments span CNN, ViT, fuzzy logic, and modular classic-to-neural pipelines. Advances ensure that these modules deliver high prediction accuracy without sacrificing transparency, facilitating adoption for safety-critical and scientific imaging domains.

1. Foundational Principles and Approaches

Interpretable image processing modules focus on encoding task-relevant domain priors directly within model architecture or optimization, enforcing transparent mappings from input features to predictions or reconstructions. Pioneering methodologies include:

• Prototype-based learning: Models such as Learnable Visual Words (LVW) abandon category-specific prototypes in favor of shared, cross-class "visual words," which are directly matched to input feature patches, yielding human-interpretable activation maps and "this-looks-like-that" rationales (Xiao et al., 2022). Contemporary advances leverage patch-level prototype selection and hierarchical scoring (e.g., PiPViT, ComFe) using ViT backbones, supporting multi-scale semantics and large-scale datasets (Oghbaie et al., 12 Jun 2025, Mannix et al., 7 Mar 2024).
• Algorithm unrolling: Deep networks are derived by truncating classical optimization routines—proximal gradient (ISTA), ADMM, or Half-Quadratic Splitting—into feedforward, trainable modules. Each layer corresponds to a well-defined step in a convex optimization or physical inverse problem, providing direct correspondence between weights and interpretable operations such as filtering, thresholding, or iterative updates (Monga et al., 2019, Gao et al., 13 Nov 2025).
• Attention and correlation-based strategies: Transformer-based modules—including the Correlator Transformer (CoTra)—model higher-order correlation structures in data via tailored attention, where weights can be directly attributed to physically meaningful n-point correlators and the order of correlation actually used in classification can be regularized and identified via L1 path analysis (Suresh et al., 31 Jul 2024).
• Modular classic-to-neural constructs: Pipelines that decompose image analysis into chains of interpretable blocks include hand-tuned or shallow-learned intensity transforms, histogram-based enhancement, morphological geometric extraction, and classic feature computation (e.g., edge, corner, or window detection (Mohammad, 9 Oct 2025); 52-feature, leaf-recognition pipeline (Lakshika et al., 2021)). CNN/U-Net-based blocks may be structurally constrained to match known operators (e.g., Frangi vesselness) (Fu et al., 2019).
• Fuzzy and logic-based modules: Intuitionistic Fuzzy Cognitive Maps (I²FCM) extend FCM graphs to quantify membership, non-membership, and hesitancy for extracted image concepts, yielding linguistically-interpretable, region-based causal graphs for final class prediction (Sovatzidi et al., 7 Aug 2024).

2. Prototype- and Attention-Based Modules: Architecture and Interpretability

Prototype and attention-based modules constitute a major axis of recent developments. In LVW (Xiao et al., 2022), the module wraps a backbone CNN by pooling M learnable visual word vectors, each scoring input feature map patches using inverted-ℓ₂ distance. Cross-entropy, clustering, and attention-fidelity losses jointly enforce agreement with the base classifier and highlight salient regions corresponding to predictions. Prototype projections onto actual image patches guarantee semantic alignment, enabling direct visualization.

ComFe (Mannix et al., 7 Mar 2024) leverages frozen ViT embeddings, transformer-decoder-generated image prototypes, and class prototypes, producing probabilistic assignments at the patch- and prototype-level, with explanations constructed by aggregating patch → prototype → class relations. The design achieves consistent, scalable performance across both fine-grained and large-scale benchmarks, supporting explicit inspection of component features via color-coded or nearest-neighbor overlays.

PiPViT (Oghbaie et al., 12 Jun 2025) further generalizes patch-based prototype interpretability by encoding part-prototypes as fixed transformer channels, extracting pixel-space explanations using per-patch channel softmax similarities and global max-pooling. Coupled with self-supervised contrastive and uniformity losses, this module achieves explanations that align closely with clinical markers in retinal imaging.

All such designs support explicit localization, allow user-inspection of patch–prototype associations, and, in their latest instantiations, maintain or approach state-of-the-art accuracy.

3. Algorithm Unrolling, Deep Unfolding, and Physically Constrained Modules

Unrolling-based modules map iterative optimization steps directly onto neural network layers, fusing efficient learning with explicit physical or mathematical reasoning:

• LISTA and TISTA unroll ISTA and MMSE shrinkage, using shared or layer-specific weight matrices and non-linearities that are directly interpretable as thresholding or data-consistency steps. These models yield both runtime acceleration and transparency, with each layer mathematically defined (Monga et al., 2019).
• InterIR (Gao et al., 13 Nov 2025) generalizes to multi-degradation restoration by mapping an ISN-based optimization, where each unrolled module updates image, spatial degradation, and feature-domain variables using explainable convolutions. Per-sample gating is made explicit by modulating kernel weights with input-derived masks, and the full modular abstraction guarantees direct correspondence to the terms of a physically interpretable optimization problem.
• Modular pipelines rooted in classic operators—e.g., edge-preserving U-Net pre-processing plus Frangi-Net segmentation for retinal vessel analysis—retain both mathematical transparency and cross-modality transferability without retraining (Fu et al., 2019).

The interpretability of such modules is underpinned by their one-to-one mapping with steps of an explicitly written optimization or known image-processing operator.

4. Modular Classic-Pipeline Blocks and Feature Extraction

Transparent pipelines in classic image processing leverage modular, deterministic building blocks, each with explicit mathematical, algorithmic, and parameteric meaning:

• Quantization and enhancement: Grayscale step quantization, histogram equalization (RGB/YCrCb), HSV-based brightness modulation, and fixed-kernel (or unsharp masking) sharpening are all parameterized by directly tunable thresholds, plateau levels, and kernel matrices, each with clear effect on output representation and interpretability (Mohammad, 9 Oct 2025).
• Geometric extraction: Canny edge, Hough-line, Harris corner, and morphological operations yield interpretable edge/shape descriptors whose parameters can be explicitly chosen or adapted, facilitating real-time or human-in-the-loop tuning.
• Hand-crafted feature pipelines: For leaf image classification, a sequence of channel conversion, denoising, global Otsu thresholding, morphological filtering, and explicit measurement of a 52-feature vector (including shape, color, Haralick texture, scagnostics) leads to fully auditable, non-learned segmentation and recognition pipelines (Lakshika et al., 2021). These features can be subjected to LDA/PCA for dimensionality reduction with full transparency of loadings.

Such modular classic pipelines remain widely adopted for their deterministic behavior, interpretability, and controllable performance profiles.

5. Fuzzy Cognitive and Logic-Oriented Modules

Fuzzy-based modules, such as I²FCM (Sovatzidi et al., 7 Aug 2024), provide inherently interpretable reasoning by structuring image region features into a graph of concept nodes. Each node's activation is described by membership, non-membership, and hesitancy values, and reasoning propagates through intuitionistic fuzzy logic, culminating in class assignment with explicit linguistic justification and confidence quantification. All interconnections and transformations result from unsupervised clustering and data-driven fuzzy set partitioning, without black-box learned weights. This approach yields both semantic region-level explanations and per-decision uncertainty, which are valuable in domains requiring traceable rationales.

6. Quantitative Evaluation and Benchmarking

Interpretability modules are now regularly benchmarked on both classical and deep image classification and restoration datasets using:

• Prediction accuracy: Standard top-1 or F1 metrics, typically matching or closely trailing non-interpretable baselines, with demonstrable SOTA on several image domains (e.g., PiPViT achieves AUC = 0.978 on retinal OCT).
• Interpretability metrics: IoU-coverage (overlap between model’s attention and reference saliency/Grad-CAM maps), region/pixel correlation with ground truth, insertion/deletion scores for saliency, and human-expert grading of module outputs (e.g., expert-blinded scores for OCT-A improvement(Fu et al., 2019)).
• Semantic/clinical precision: Average precision (AP) for prototype localization in detection tasks, as with PiPViT for drusen detection (Oghbaie et al., 12 Jun 2025).
• Functional auditability: Direct inversion of input → output mapping, or audit of feature contributions via LDA/PCA in classic pipelines.

7. Module Deployment, Scalability, and Future Directions

Modern interpretable modules exhibit progress in scalability (e.g., ComFe supports ImageNet-scale with fixed hyperparameters (Mannix et al., 7 Mar 2024)), task compatibility (segmentation, detection, temporal extension), and plug-and-play integration with standard backbones (CNN, ViT, hybrid, or sequence models). The field is trending toward:

• Relaxing prototype specificity to boost generalization, optimizing for dual fidelity (classification and interpretable localization) (Xiao et al., 2022).
• Unified integration with multi-resolution, multi-task pipelines (e.g., PiPViT (Oghbaie et al., 12 Jun 2025)) and cross-modal transfer (Fu et al., 2019).
• Systematic benchmarking of both interpretability and predictive performance, as interpretability moves from illustrative narrative to quantifiable, task-critical module property.

Potential avenues include joint optimization of prototype and backbone representations, hierarchical abstraction of component features, longitudinal audit trails, and human-in-the-loop parameter steering, especially for high-consequence domains (medical, scientific, legal, embodied AI).

---

References

• Learnable Visual Words for Interpretable Image Recognition (Xiao et al., 2022)
• Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification (Sovatzidi et al., 7 Aug 2024)
• Eigen-spectrograms: An interpretable feature space... (Brusa et al., 2021)
• Interpretable 2D Vision Models for 3D Medical Images (Ziller et al., 2023)
• Algorithm Unrolling: Interpretable, Efficient Deep Learning... (Monga et al., 2019)
• ComFe: An Interpretable Head for Vision Transformers (Mannix et al., 7 Mar 2024)
• Hierarchical Spatial Algorithms for High-Resolution Image Quantization... (Mohammad, 9 Oct 2025)
• Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse (Fu et al., 2019)
• Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution (Gao et al., 13 Nov 2025)
• Interpretable correlator Transformer for image-like quantum matter data (Suresh et al., 31 Jul 2024)
• Computer-aided Interpretable Features for Leaf Image Classification (Lakshika et al., 2021)
• Decoupling Deep Learning for Interpretable Image Recognition (Peng et al., 2022)
• PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis (Oghbaie et al., 12 Jun 2025)