Modular Neural ISP Framework

• The modular neural ISP framework is a pipeline architecture that transforms raw sensor data through clearly defined, parameterized modules for enhanced control and versatility.
• It fuses classic operators with learned neural modules to execute denoising, color correction, tone mapping, and detail enhancement in a reconfigurable pipeline.
• Empirical evaluations show that this modular approach outperforms traditional and monolithic methods by offering superior flexibility, efficiency, and cross-camera generalization.

A modular neural image signal processing (ISP) framework refers to a pipeline architecture for rendering display-referred images from raw sensor data, which decomposes the overall imaging task into a sequence of distinct, explicitly parameterized or learned stages. This modularity introduces fine-grained control, interpretability, scalability, and adaptability, distinguishing these frameworks from both hand-designed fixed ISPs and monolithic end-to-end neural networks (Afifi et al., 9 Dec 2025). The modular approach dominates recent learning-based ISP research, with state-of-the-art results and architectural flexibility demonstrated across diverse imaging and computational photography applications.

1. Modular ISP Pipeline Architecture

A typical modular neural ISP framework processes an input raw Bayer image through a sequence of discrete stages, each responsible for a well-defined transformation or correction. The canonical stages in leading frameworks are demosaicing, denoising, color correction (white balance and color-correction matrix), global and local tone mapping, chroma mapping, gamma correction, upsampling/refinement, and detail enhancement (Afifi et al., 9 Dec 2025, Kim et al., 2023). Each is implemented as either a parameterized neural module, a hand-crafted or classical operator, or a hybrid with learned parameters and plug-and-play capacity.

A representative pipeline structure is as follows (Afifi et al., 9 Dec 2025):

1. Raw Denoising: Encoder–decoder network (e.g., NAFNet variant), operating on the demosaiced RGB representation for noise suppression.
2. Color Correction: Application of a camera-specific white balance (diagonal matrix) and a $3\times3$ color-correction matrix, parameterized and optionally trained per camera.
3. Photofinishing: Modular chain at lower spatial resolution (often 1/4 scale), including:
   • Digital gain estimation,
   • Global tone mapping (e.g., channel-wise power-law or rational transforms),
   • Local tone mapping (spatially-adaptive nonlinearities via learned guidance and coefficient grids),
   • Chroma mapping (residual 2D LUT learned per style, optionally image-adaptive or user-modifiable),
   • (Optional) global or per-style 3D LUT for artistic effects.
4. Gamma Correction: Parametric gamma operator for rendering sRGB-like outputs.
5. Guided Upsampling: Upscaling of the photofinished output back to full image size, using a differentiable bilateral grid or guided filter.
6. Detail Enhancement: Shallow residual encoder–decoder networks to sharpen and refine spatial detail before final output.

Modules operate at distinct spatial scales as needed for efficiency and global context integration. Each stage exposes explicit parameters or controllers for re-training or user interaction, and supports replacement, removal, or augmentation for task-specific adaptation (Afifi et al., 9 Dec 2025, Yu et al., 2021).

2. Module Design and Parameterization

Each module is architected for both interpretability and effective training. Examples from (Afifi et al., 9 Dec 2025) include:

• Denoising (D_raw): Multi-scale NAFNet variants with variable width and depth; loss is $\ell_1$ on raw patches.
• Color Correction: Matrix operators (white balance, CCM), with learnable or fixed parameters.
• Digital Gain and Global Tone Mapping: Small CNNs output per-image gain/transfer parameters, e.g.,

$$f_\text{TM}(x; a, b, c) = \frac{x^a}{x^a + [c\cdot(1-x)]^b}$$

for channel-wise tone adjustment.

• Local Tone Mapping: Multi-head attention or grid-based predictors produce spatially varying tone parameters; modulation realized via trilinear interpolation.
• Chroma Mapping: Residual 2D LUTs learned by an encoder–decoder acting on differentiable histograms of YCbCr channels, combined with learnable base LUTs.
• Gamma, Upsampling, Detail Enhancement: Compact CNNs or gated operator blocks.

Parameterization is chosen to enable easy replacement of traditional operators (e.g., manual LUTs, polynomial curves) with learning-based analogs, and to enable efficient fine-tuning for cross-camera generalization or style transfer (Afifi et al., 9 Dec 2025, Kim et al., 2023, Yu et al., 2021).

Configuration and partial re-training is often sufficient: to adapt to a new camera, only the front-end denoising or color correction networks are changed; to create a new rendering style, only photofinishing and enhancement nets are retrained (Afifi et al., 9 Dec 2025).

3. Learning, Loss Functions, and Optimization

Training modular neural ISP frameworks proceeds with multi-term loss functions to balance fidelity, perceptual quality, and structural or color accuracy. Typical aggregate photofinishing loss (for the $H/4 \times W/4$ output) includes:

$$L_\text{total} = \lambda_1\,\ell_1 + \lambda_\text{SSIM}\,\ell_\text{SSIM} + \lambda_{\Delta E}\,\ell_{\Delta E} + \lambda_\text{perc}\,\ell_\text{perc} + \lambda_\text{CbCr}\,\ell_\text{CbCr} + \cdots$$

where components are, e.g., mean absolute error, (1–)SSIM, mean ΔE in Lab space, VGG-19 feature norm, mean chroma error, TV on LUTs/maps, and channel-mean luminance (Afifi et al., 9 Dec 2025).

Training regimes typically include staged optimization:

• Stagewise pre-training of denoising and photofinishing modules with raw and curated ground truths (e.g., aligned high-quality photographs).
• Joint fine-tuning on the target dataset or for novel style rendering.
• Explicit adaptation for efficiency-oriented or style-oriented variants (e.g., ReconfigISP “Fast”/“Faster” using latency-weighted losses) (Yu et al., 2021).

Auxiliary modules (e.g., for parameter conditioning or global context) are separately trained or co-optimized via auxiliary losses, ensuring robustness across varying EXIF, ISO, and environmental conditions (Kim et al., 2023).

4. Flexibility, Generalization, and User Control

A hallmark of modular neural ISPs is the explicit separation of camera-specific modules (e.g., denoiser, color correction) from camera-agnostic photofinishing and style networks (Afifi et al., 9 Dec 2025). This stratification yields several practical advantages:

• Cross-Camera Generalization: New cameras are supported by retraining or exchanging only the low-level modules; style transfer between camera models reduces to recombination of per-stage learned parameters (Kim et al., 2023).
• Task-Specific Adaptation: Downstream requirements (e.g., human-preferred “look,” machine vision accuracy) are met by modifying or replacing only selected modules (Afifi et al., 9 Dec 2025, Yu et al., 2021).
• User-Interactive Editing: A fully modular neural ISP can be embedded in a rendering tool that allows after-capture manipulation of exposure, contrast, color grading, highlights, and even artistic LUTs, leveraging stored raw data and intermediate parameters (Afifi et al., 9 Dec 2025).
• Debuggability and Transparency: Inspecting the output after each module supports diagnosis and troubleshooting of artifacts or color infidelity.

This design enables reusing modules across different image resolutions, sensor types, and picture styles with minimal recompilation or retraining, improving scalability and reducing compute/memory overhead (Afifi et al., 9 Dec 2025, Yu et al., 2021).

5. Performance and Comparative Evaluation

Recent modular neural ISPs consistently outperform both traditional ISPs and earlier monolithic CNN-based pipelines on benchmarks including S24, MIT-Adobe 5K, and cross-sensor tests (Afifi et al., 9 Dec 2025). Quantitative results from (Afifi et al., 9 Dec 2025) highlight the efficiency and compactness of modular designs:

Method	PSNR (↑)	SSIM (↑)	LPIPS (↓)	ΔE2000 (↓)	#params
LiteISP	25.49	0.897	0.074	5.521	9.1M
Ours (lite)	26.36	0.878	0.071	4.413	0.45M
Ours (base)	27.52	0.922	0.055	3.938	1.19M
Ours (large)	27.57	0.923	0.054	3.913	3.89M

Comparison to other modular methods demonstrates significant parameter count and computational cost reduction for similar or better accuracy—e.g., TA-ISP achieves 0.003M parameters and 0.20 GFLOPs on 4K RAW images, compared to 9.14M parameters and 1690 GFLOPs for MW-ISPNet (Chen et al., 17 Sep 2025).

Further, modular ISPs enable dynamic trade-off between runtime and quality, as in AdaptiveISP, where average composition varies between 2.9–4.1 modules (latency 9–15 ms) with only ~1.5% mAP drop for real-time detection (Wang et al., 30 Oct 2024).

6. Extensions and Emerging Research Directions

The modular paradigm has enabled a variety of architectural and functional innovations:

• Parametric and Conditioned ISPs: Modules like ParamNet convert camera EXIF parameters (ISO, exposure, aperture, focal length) into feature vectors which condition downstream local/global transforms, enabling high-fidelity RAW↔sRGB mapping for arbitrary settings (Kim et al., 2023).
• Task-Driven and Dynamic ISP Construction: RL-based controllers generate image-dependent per-frame pipelines (structure and parameters) for detection, maximizing accuracy–cost tradeoff under dynamic scenes (Wang et al., 30 Oct 2024).
• Data-Efficient Optimization: Bilevel/differentiable NAS (ReconfigISP) searches for optimal module sequences and parameters with proxy networks, yielding accurate, compact pipelines from limited data (Yu et al., 2021).
• Interactive and Style-Editable Rendering: Fine-grained user control over editing parameters, enable/disable of stages, and style blending through explicit modularity and parameter exposure (Afifi et al., 9 Dec 2025).
• Global Context Guidance: Integration of global context modules after early pipeline stages improves color constancy and scene illumination estimation without introducing significant computational burden (Elezabi et al., 17 Apr 2024).

Possible future research includes invertible ISP models with parameteric controls, richer per-module conditioning (e.g., for vignetting, per-pixel defects), more sophisticated temporal smoothing for video, and extension of the modular approach to non-standard sensing modalities.

7. Objective Assessment and Remaining Challenges

The modular ISP framework provides a middle ground between rigid, handcrafted traditional pipelines and fully end-to-end deep architectures. Key strengths are interpretability, efficiency, cross-device transfer, and customizability, while retaining or exceeding state-of-the-art fidelity on major public benchmarks (Afifi et al., 9 Dec 2025, Kim et al., 2023, Yu et al., 2021).

However, the approach does incur increased pipeline complexity and an engineering burden for module design, validation, and maintenance; full cross-domain generalization and robustness to extreme, unseen camera artifacts remain open challenges. Potential failure cases may stem from distributional shifts in camera response, breakdowns in module independence, or misalignment between human and machine perceptual objectives.

In summary, modular neural ISP architectures now represent the dominant paradigm in raw-to-RGB rendering research, supporting both practical deployment in high-performance photo-editing tools and scalable adaptation across camera hardware and downstream tasks (Afifi et al., 9 Dec 2025, Kim et al., 2023, Chen et al., 17 Sep 2025, Wang et al., 30 Oct 2024, Yu et al., 2021).