DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping

• URL: http://arxiv.org/abs/2604.03402v1
• Date: Fri, 03 Apr 2026 19:04:36 GMT
• Title: DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping
• Abstract summary: DRIFT (Deep Restoration, ISP Fusion, and Tone-mapping) is an efficient AI mobile camera pipeline.<n>It generates high quality RGB images from hand-held raw captures.
• Score: 8.13037513173521
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Smartphone cameras have gained immense popularity with the adoption of high-resolution and high-dynamic range imaging. As a result, high-performance camera Image Signal Processors (ISPs) are crucial in generating high-quality images for the end user while keeping computational costs low. In this paper, we propose DRIFT (Deep Restoration, ISP Fusion, and Tone-mapping): an efficient AI mobile camera pipeline that generates high quality RGB images from hand-held raw captures. The first stage of DRIFT is a Multi-Frame Processing (MFP) network that is trained using a adversarial perceptual loss to perform multi-frame alignment, denoising, demosaicing, and super-resolution. Then, the output of DRIFT-MFP is processed by a novel deep-learning based tone-mapping (DRIFT-TM) solution that allows for tone tunability, ensures tone-consistency with a reference pipeline, and can be run efficiently for high-resolution images on a mobile device. We show qualitative and quantitative comparisons against state-of-the-art MFP and tone-mapping methods to demonstrate the effectiveness of our approach.

Related papers

• Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection [45.70137072978864]
  We propose a novel tone mapping method that bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks.<n>Our pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on NVIDIA Jetson platforms.
  arXiv  Detail & Related papers  (2026-08-31T07:52:53Z)
• High-Speed Full-Color HDR Imaging via Unwrapping Modulo-Encoded Spike Streams [83.83721273785281]
  We present a complete modulo-based HDR imaging system that enables high-speed, full-color HDR acquisition.<n>At the core of our approach is an exposure-decoupled formulation of modulo imaging that allows multiple measurements to be interleaved in time.<n>To validate the practical viability of our system, we demonstrate a proof-of-concept hardware implementation based on modulo-encoded spike streams.
  arXiv  Detail & Related papers  (2026-04-16T05:20:38Z)
• SuperF: Neural Implicit Fields for Multi-Image Super-Resolution [15.679463301639329]
  SuperF is a test-time optimization approach for multi-image super-resolution (MISR)<n>Our experiments yield compelling results on simulated bursts of satellite imagery and ground-level images from handheld cameras.
  arXiv  Detail & Related papers  (2025-12-09T20:57:25Z)
• Learned Lightweight Smartphone ISP with Unpaired Data [55.2480439325792]
  We propose a novel training method for a learnable Image Signal Processor (ISP)<n>Our unpaired approach employs a multi-term loss function guided by adversarial training.<n>Compared to paired training methods, our unpaired learning strategy shows strong potential and achieves high fidelity.
  arXiv  Detail & Related papers  (2025-05-15T15:37:51Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Balancing Efficiency and Quality: MoEISR for Arbitrary-Scale Image Super-Resolution [11.256166497045792]
  We introduce a novel and efficient framework, the Mixture-of-Experts Implicit Super-Resolution (MoEISR)<n>MoEISR dynamically allocates the most suitable decoding expert to each pixel using a lightweight mapper module.<n>Our experiments demonstrate that MoEISR successfully reduces significant amount of floating point operations (FLOPs) while delivering comparable or superior peak signal-to-noise ratio (PSNR)
  arXiv  Detail & Related papers  (2023-11-20T05:34:36Z)
• RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution [57.98314517861539]
  Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network.
  arXiv  Detail & Related papers  (2023-06-30T12:14:13Z)
• RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network [23.451063587138393]
  We propose a Raw Burst Super-Resolution Iterative Convolutional Neural Network (RBSRICNN) The proposed network produces the final output by an iterative refinement of the intermediate SR estimates. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.
  arXiv  Detail & Related papers  (2021-10-25T19:01:28Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors [21.264746234523678]
  Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors. In this paper, we attempt to leverage complementary information from a low-cost channel (visible/depth) to boost image quality of an expensive channel (thermal) using fewer parameters.
  arXiv  Detail & Related papers  (2020-12-07T02:15:28Z)

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.