We introduce gradient-domain rendering for Monte Carlo image synthesis. While
previous gradient-domain Metropolis Light Transport sought to distribute more
samples in areas of high gradients, we show, in contrast, that estimating image
gradients is also possible using standard (non-Metropolis) Monte Carlo
algorithms, and furthermore, that even without changing the sample distribution,
this often leads to significant error reduction. This broadens the applicability
of gradient rendering considerably. To gain insight into the conditions under
which gradient-domain sampling is beneficial, we present a frequency analysis
that compares Monte Carlo sampling of gradients followed by Poisson
reconstruction to traditional Monte Carlo sampling. Finally, we describe
Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the
standard path tracing algorithm that can yield far superior results.

One more link ... on this topic:
https://mediatech.aalto.fi/publications/graphics/GPT/

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