Given any metric that compares images of di erent dynamic
range, we propose a method to reduce their distance with respect to this
metric. The key idea is to consider the metric as a non local operator.
Then, we transform the problem of distance reduction into a non local
variational problem. In this context, the low dynamic range image having
the smallest distance with a given high dynamic range is the minimum
of a suitable energy, and can be reached through a gradient descent
algorithm. ...
Given any metric that compares images of di erent dynamic
range, we propose a method to reduce their distance with respect to this
metric. The key idea is to consider the metric as a non local operator.
Then, we transform the problem of distance reduction into a non local
variational problem. In this context, the low dynamic range image having
the smallest distance with a given high dynamic range is the minimum
of a suitable energy, and can be reached through a gradient descent
algorithm. Dealing with an appropriate metric, we present an application
to Tone Mapping Operator (TMO) optimization. We apply our gradient
descent algorithm, where the initial conditions are Tone Mapped (TM)
images. Experiments show that our algorithm does reduce the distance
of the TM images with the high dynamic range source images, meaning
that our method improves the corresponding TMOs.
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