Restoration and Enhancement of Historical Stereo Photos †
- With respect to SMR, the novel SMR+ is redesigned so as to better preserve finer details while at the same time improving further the restoration quality. This is accomplished by employing supersampling  at the image fusion step in conjunction with a weighting scheme guided by the original restoration approach.
- The recent state-of-the-art deep network BOPBtL , specifically designed for old photo restoration, is now included in the comparison, both as standalone and to serve as post-processing of SMR+.
- The collection of historical stereo photos employed as a dataset is roughly doubled to provide a more comprehensive evaluation.
- The use of renowned image quality assessment metrics is investigated and discussed for these kinds of applications.
Note: To ease the inspection and the comparison of the different images presented, an interactive PDF document is provided in the additional material (https://drive.google.com/drive/folders/1Fmsm50bMMDSd0z4JXOhCZ3hPDIXdwMUL) to allow readers to view each image at its full dimensions and quickly switch to the other images to be compared.
2. Proposed Method
2.1. Auxiliary Image Pointwise Transfer
2.2. Color Correction
2.3. Data Fusion
- Detection. A binary correction mask is computed by considering the error image the local window centered at each . Given as the subset of pixels with intensity values lower than the 66% percentile on , the pixel is marked as requiring adjustment if the square root of the average intensity value on is higher than (chosen experimentally). This results in a binary correction mask B that is smoothed with a Gaussian kernel and then binarized again by a threshold value of 0.5. As clear from Figure 6a, using the percentile-based subset is more robust than working with the whole window .
- Adjustment. Data fusion is repeated again after updating pixels on that need to be adjusted with the corresponding ones of . Since is a sort of average between and , the operation just described pushes marked pixels towards . At the end of this step, the gradient enhanced image is also updated accordingly and, in case of no further iterations, it constitutes the final output.
2.5. Guided Supersampling
3.2. Compared Methods
4. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Figure 1 and Figure 9||BRISQUE||41.89||54.34||51.47||53.11||43.46||24.15||24.20|
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Fanfani, M.; Colombo, C.; Bellavia, F. Restoration and Enhancement of Historical Stereo Photos. J. Imaging 2021, 7, 103. https://doi.org/10.3390/jimaging7070103
Fanfani M, Colombo C, Bellavia F. Restoration and Enhancement of Historical Stereo Photos. Journal of Imaging. 2021; 7(7):103. https://doi.org/10.3390/jimaging7070103Chicago/Turabian Style
Fanfani, Marco, Carlo Colombo, and Fabio Bellavia. 2021. "Restoration and Enhancement of Historical Stereo Photos" Journal of Imaging 7, no. 7: 103. https://doi.org/10.3390/jimaging7070103