Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

LiTMNet: A deep CNN for efficient HDR image reconstruction from a single LDR image

Wu, Guotao, Song, Ran, Zhang, Mingxin, Li, Xiaolei and Rosin, Paul L. ORCID: 2022. LiTMNet: A deep CNN for efficient HDR image reconstruction from a single LDR image. Pattern Recognition 127 , 108620. 10.1016/j.patcog.2022.108620

[thumbnail of LiTMNet-PR-postprint (1).pdf]
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (19MB) | Preview


Existing methods can generate a high dynamic range (HDR) image from a single low dynamic range (LDR) image using convolutional neural networks (CNNs). However, they are too cumbersome to run on mobile devices with limited computational resources. In this work, we design a lightweight CNN, namely LiTMNet which takes a single LDR image as input and recovers the lost information in its saturated regions to reconstruct an HDR image. To avoid trading off the reconstruction quality for efficiency, LiTMNet does not only adapt a lightweight encoder for efficient feature extraction, but also contains newly designed upsampling blocks in the decoder to alleviate artifacts and further accelerate the reconstruction. The final HDR image is produced by nonlinearly blending the network prediction and the original LDR image. Qualitative and quantitative comparisons demonstrate that LiTMNet produces HDR images of high quality comparable with the current state of the art and is faster as tested on a mobile device. Please refer to the supplementary video for additional visual results.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0031-3203
Date of First Compliant Deposit: 22 March 2022
Date of Acceptance: 1 March 2022
Last Modified: 06 Nov 2023 13:59

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics