DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913023907594240 |
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| author | Majee, Soumendu Ebenezer, Joshua Peter Venkataramanan, Abhinau K. Liu, Weidi Balke, Thilo Nadir, Zeeshan Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim |
| author_facet | Majee, Soumendu Ebenezer, Joshua Peter Venkataramanan, Abhinau K. Liu, Weidi Balke, Thilo Nadir, Zeeshan Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping Majee, Soumendu Ebenezer, Joshua Peter Venkataramanan, Abhinau K. Liu, Weidi Balke, Thilo Nadir, Zeeshan Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.03402 |