{"title":"Improved high dynamic range imaging using multi-scale feature flows balanced between task-orientedness and accuracy","authors":"Qian Ye , Masanori Suganuma , Takayuki Okatani","doi":"10.1016/j.cviu.2024.104126","DOIUrl":null,"url":null,"abstract":"<div><p>Deep learning has made it possible to accurately generate high dynamic range (HDR) images from multiple images taken at different exposure settings, largely owing to advancements in neural network design. However, generating images without artifacts remains difficult, especially in scenes with moving objects. In such cases, issues like color distortion, geometric misalignment, or ghosting can appear. Current state-of-the-art network designs address this by estimating the optical flow between input images to align them better. The parameters for the flow estimation are learned through the primary goal, producing high-quality HDR images. However, we find that this ”task-oriented flow” approach has its drawbacks, especially in minimizing artifacts. To address this, we introduce a new network design and training method that improve the accuracy of flow estimation. This aims to strike a balance between task-oriented flow and accurate flow. Additionally, the network utilizes multi-scale features extracted from the input images for both flow estimation and HDR image reconstruction. Our experiments demonstrate that these two innovations result in HDR images with fewer artifacts and enhanced quality.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1077314224002078/pdfft?md5=35e8f40c73b01f0b9afae0db47e39486&pid=1-s2.0-S1077314224002078-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002078","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Deep learning has made it possible to accurately generate high dynamic range (HDR) images from multiple images taken at different exposure settings, largely owing to advancements in neural network design. However, generating images without artifacts remains difficult, especially in scenes with moving objects. In such cases, issues like color distortion, geometric misalignment, or ghosting can appear. Current state-of-the-art network designs address this by estimating the optical flow between input images to align them better. The parameters for the flow estimation are learned through the primary goal, producing high-quality HDR images. However, we find that this ”task-oriented flow” approach has its drawbacks, especially in minimizing artifacts. To address this, we introduce a new network design and training method that improve the accuracy of flow estimation. This aims to strike a balance between task-oriented flow and accurate flow. Additionally, the network utilizes multi-scale features extracted from the input images for both flow estimation and HDR image reconstruction. Our experiments demonstrate that these two innovations result in HDR images with fewer artifacts and enhanced quality.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems