2021/04/19 by S M A Sharif, SMA Sharif, Sharif, SMA +6 · 1 citation
Computer Science · Engineering · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Artificial intelligence #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Dynamic range #Engineering #FOS: Computer and information sciences #High dynamic range #High-dynamic-range imaging #Image (mathematics) #Image Enhancement Techniques #Range (aeronautics) #Set (abstract data type) #Task (project management) #Tone mapping #Translation (biology) #cs.CV
paper · pdf · doi:10.48550/arxiv.2104.09386
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/04/19 · openalex publication_date 2021/04/19 · arxiv updated 2021/04/20 · openalex created_date 2021/04/26 · openalex updated_date 2026/08/08
Mapping a single exposure low dynamic range (LDR) image into a high dynamic range (HDR) is considered among the most strenuous image to image translation tasks due to exposure-related missing information. This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network. Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings. Therefore, we aim to perform image enhancement task like denoising, exposure correction, etc., in the first stage. Additionally, the second stage of our deep network learns tone mapping and bit-expansion from a convex set of data samples. The qualitative and quantitative comparisons demonstrate that the proposed method can outperform the existing LDR to HDR works with a marginal difference. Apart from that, we collected an LDR image dataset incorporating different camera systems. The evaluation with our collected real-world LDR images illustrates that the proposed method can reconstruct plausible HDR images without presenting any visual artefacts. Code available: https://github. com/sharif-apu/twostageHDRNTIRE21.