6533b7dcfe1ef96bd1273524
RESEARCH PRODUCT
Restoration and Enhancement of Historical Stereo Photos
Carlo ColomboMarco FanfaniFabio Bellaviasubject
image denoisingComputer sciencemedia_common.quotation_subjectNoise reductionComputer applications to medicine. Medical informaticsR858-859.7ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flow02 engineering and technologyimage restorationArticleoptical flowgradient filteringPhotography0202 electrical engineering electronic engineering information engineeringRedundancy (engineering)historical photosContrast (vision)Radiology Nuclear Medicine and imagingComputer visionimage enhancementElectrical and Electronic EngineeringTR1-1050stereo matchingImage restorationmedia_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniguided supersamplingImage fusionSettore INF/01 - Informaticabusiness.industry020206 networking & telecommunicationsSupersamplingQA75.5-76.95stacked medianComputer Graphics and Computer-Aided DesignTransmission (telecommunications)Electronic computers. Computer science020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessimage denoising image restoration image enhancement stereo matching optical flow gradient filtering stacked median guided supersampling historical photosdescription
Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.
year | journal | country | edition | language |
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2021-06-24 | Journal of Imaging |