title: Learned Lightweight Smartphone ISP with Unpaired Data
summary: The Image Signal Processor (ISP) is a fundamental component in modern
  smartphone cameras responsible for conversion of RAW sensor image data to RGB
  images with a strong focus on perceptual quality. Recent work highlights the
  potential of deep learning approaches and their ability to capture details with
  a quality increasingly close to that of professional cameras. A difficult and
  costly step when developing a learned ISP is the acquisition of pixel-wise
  aligned paired data that maps the raw captured by a smartphone camera sensor to
  high-quality reference images. In this work, we address this challenge by
  proposing a novel training method for a learnable ISP that eliminates the need
  for direct correspondences between raw images and ground-truth data with
  matching content. Our unpaired approach employs a multi-term loss function
  guided by adversarial training with multiple discriminators processing feature
  maps from pre-trained networks to maintain content structure while learning
  color and texture characteristics from the target RGB dataset. Using
  lightweight neural network architectures suitable for mobile devices as
  backbones, we evaluated our method on the Zurich RAW to RGB and Fujifilm
  UltraISP datasets. Compared to paired training methods, our unpaired learning
  strategy shows strong potential and achieves high fidelity across multiple
  evaluation metrics. The code and pre-trained models are available at
  https://github.com/AndreiiArhire/Learned-Lightweight-Smartphone-ISP-with-Unpaired-Data .
paper_authors._id: 682729c119f2b57dbb32cb26
paper_authors.name: Andrei Arhire
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T13:23:16.169Z
paper_authors._id: 677d1decafd8031632cb65a4
paper_authors.avatarUrl: /avatars/ecfc1a6dfe63cf0d92f67a4345879ba0.svg
paper_authors.fullname: Andrei Arhire
paper_authors.type: user
paper_authors.user: AndreiArhire
paper_authors._id: 682729c119f2b57dbb32cb27
paper_authors.name: Radu Timofte
paper_title: Learned Lightweight Smartphone ISP with Unpaired Data
paper_summary: The Image Signal Processor (ISP) is a fundamental component in modern
  smartphone cameras responsible for conversion of RAW sensor image data to RGB
  images with a strong focus on perceptual quality. Recent work highlights the
  potential of deep learning approaches and their ability to capture details with
  a quality increasingly close to that of professional cameras. A difficult and
  costly step when developing a learned ISP is the acquisition of pixel-wise
  aligned paired data that maps the raw captured by a smartphone camera sensor to
  high-quality reference images. In this work, we address this challenge by
  proposing a novel training method for a learnable ISP that eliminates the need
  for direct correspondences between raw images and ground-truth data with
  matching content. Our unpaired approach employs a multi-term loss function
  guided by adversarial training with multiple discriminators processing feature
  maps from pre-trained networks to maintain content structure while learning
  color and texture characteristics from the target RGB dataset. Using
  lightweight neural network architectures suitable for mobile devices as
  backbones, we evaluated our method on the Zurich RAW to RGB and Fujifilm
  UltraISP datasets. Compared to paired training methods, our unpaired learning
  strategy shows strong potential and achieves high fidelity across multiple
  evaluation metrics. The code and pre-trained models are available at
  https://github.com/AndreiiArhire/Learned-Lightweight-Smartphone-ISP-with-Unpaired-Data .
paper_ai_summary: A novel unpaired training method for a learnable ISP uses adversarial training with multiple discriminators to achieve high-quality image transformation without paired datasets.
paper_ai_keywords: learned ISP unpaired training method multi-term loss function adversarial training discriminators feature maps pre-trained networks lightweight neural network architectures Zurich RAW to RGB Fujifilm UltraISP