# ReconfigISP: Reconfigurable Camera Image Processing Pipeline arXiv: https://arxiv.org/abs/2109.04760 ยท HTML: https://mlsanity.com/paper/2109.04760/ ## Key facts - Cited by 30 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 32 references that are in the map. - First posted on arXiv 10 Sep 2021. ## Builds on (most cited first, 14 of 14) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018) - [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018) - [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016) - [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014) - [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018) - [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018) - [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016) - [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018) - [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018) - [DeepISP: Towards Learning an End-to-End Image Processing Pipeline](https://mlsanity.com/paper/1801.06724/) (2018) - [Path-Restore: Learning Network Path Selection for Image Restoration](https://mlsanity.com/paper/1904.10343/) (2019) - [AWNet: Attentive Wavelet Network for Image ISP](https://mlsanity.com/paper/2008.09228/) (2020) - [Exposure: A White-Box Photo Post-Processing Framework](https://mlsanity.com/paper/1709.09602/) (2017) ## Built on by (most cited first, 30 of 30) - [JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent](https://mlsanity.com/paper/2506.17612/) (2025) - [Bilateral Guided Radiance Field Processing](https://mlsanity.com/paper/2406.00448/) (2024) - [DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition](https://mlsanity.com/paper/2211.01146/) (2022) - [Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision](https://mlsanity.com/paper/2112.04719/) (2021) - [RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images](https://mlsanity.com/paper/2408.14802/) (2024) - [AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection](https://mlsanity.com/paper/2410.22939/) (2024) - [Image Quality Assessment: From Human to Machine Preference](https://mlsanity.com/paper/2503.10078/) (2025) - [Learning Degradation-Independent Representations for Camera ISP Pipelines](https://mlsanity.com/paper/2307.00761/) (2023) - [Inverting the Imaging Process by Learning an Implicit Camera Model](https://mlsanity.com/paper/2304.12748/) (2023) - [Towards RAW Object Detection in Diverse Conditions](https://mlsanity.com/paper/2411.15678/) (2024) - [Goal Conditioned Reinforcement Learning for Photo Finishing Tuning](https://mlsanity.com/paper/2503.07300/) (2025) - [All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines](https://mlsanity.com/paper/2112.09219/) (2021) - [Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection](https://mlsanity.com/paper/2303.13916/) (2023) - [Beyond RGB: Adaptive Parallel Processing for RAW Object Detection](https://mlsanity.com/paper/2503.13163/) (2025) - [RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model](https://mlsanity.com/paper/2411.11717/) (2024) - [RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling](https://mlsanity.com/paper/2605.05941/) (2026) - [SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements](https://mlsanity.com/paper/2503.07101/) (2025) - [RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images and A Benchmark](https://mlsanity.com/paper/2503.17027/) (2025) - [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026) - [Seeing Far in the Dark with Patterned Flash](https://mlsanity.com/paper/2207.12570/) (2022) - [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023) - [POS-ISP: Pipeline Optimization at the Sequence Level for Task-aware ISP](https://mlsanity.com/paper/2604.06938/) (2026) - [UniISP: A Unified ISP Framework for Both Human and Machine Vision](https://mlsanity.com/paper/2605.07359/) (2026) - [One-shot Detail Retouching with Patch Space Neural Transformation Blending](https://mlsanity.com/paper/2210.01217/) (2022) - [eXplainable Artificial Intelligence on Medical Images: A Survey](https://mlsanity.com/paper/2305.07511/) (2023) - [Probabilistic Directed Distance Fields for Ray-Based Shape Representations](https://mlsanity.com/paper/2404.09081/) (2024) - [Lightweight Unpaired Smartphone ISP Transfer with Semantic Pseudo-Pairing](https://mlsanity.com/paper/2605.07495/) (2026) - [FreqAdapt: Frequency-Adaptive Processing for RAW Object Detection](https://mlsanity.com/paper/2608.03385/) (2026) - [Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection](https://mlsanity.com/paper/2608.30400/) (2026) - [RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation](https://mlsanity.com/paper/2609.37530/) (2026) ## Abstract (arXiv) Image Signal Processor (ISP) is a crucial component in digital cameras that transforms sensor signals into images for us to perceive and understand. Existing ISP designs always adopt a fixed architecture, e.g., several sequential modules connected in a rigid order. Such a fixed ISP architecture may be suboptimal for real-world applications, where camera sensors, scenes and tasks are diverse. In this study, we propose a novel Reconfigurable ISP (ReconfigISP) whose architecture and parameters can be automatically tailored to specific data and tasks. In particular, we implement several ISP modules, and enable backpropagation for each module by training a differentiable proxy, hence allowing us to leverage the popular differentiable neural architecture search and effectively search for the optimal ISP architecture. A proxy tuning mechanism is adopted to maintain the accuracy of proxy networks in all cases. Extensive experiments conducted on image restoration and object detection, with different sensors, light conditions and efficiency constraints, validate the effectiveness of ReconfigISP. Only hundreds of parameters need tuning for every task.