Fig 1.
Multi exposure data sets used for comparison.
Fig 2.
Comparison between applying the fusion to each RGB channel independently and using the YUV luminance and chromatic components.
The luminance of the two results is identical, while the color is enhanced by the latter strategy.
Fig 3.
Noisy example with sequence office and noise with standard deviation σ = 15.
Top, from left to right: one image from the multi-exposure set, fusion without DCT thresholding, fusion with DCT thresholding. Bottom: detail of the images.
Fig 4.
Processing scheme of a specific reference patch.
Fig 5.
Results of fusion of noise-free multi-exposure images with different methods.
Fig 6.
Excerpt of the results shown in Fig 5.
Fig 7.
Results of fusion of noisy multi-exposure images with different methods.
The noise standard deviation of each input image is 15.
Fig 8.
Noisy multi-exposure data sets used for comparison.
On the first row, the noise standard deviation of each input image is 15. On the second row the standard deviation is 25.
Fig 9.
Excerpt of the results shown in Fig 8.
It is clear from this figure, that our method is the only one that takes noise into account.
Fig 10.
Results of fusion and denoising.
Our method is the only one applied directly to the noisy multi-exposure images. The rest of methods fuse denoised versions of the images obtained using the BM3D algorithm. The noise standard deviation of each input image is 25.
Fig 11.
Excerpt of the results shown in Fig 10.
Table 1.
Fig 12.
From left to right: three noisy images with different exposures and the fusion and noise removal result. Below an excerpt of each image.