Guided image contrast enhancement based on retrieved images in cloud
1. Guided Image Contrast Enhancement Based on Retrieved Images in Cloud
Abstract:
We propose a guided image contrast enhancement framework based
on cloud images, in which the context- sensitive and context-free contrast is
jointly improved via solving a multi-criteria optimization problem. In particular,
the context-sensitive contrast is improved by performing advanced unsharp
masking on the input and edge-preserving filtered images, while the context-free
contrast enhancement is achieved by the sigmoid transfer mapping. To
automatically determine the contrast enhancement level, the parameters in the
optimization process are estimated by taking advantages of the retrieved images
with similar content. For the purpose of automatically avoiding the involvement
of low-quality retrieved images as the guidance, a recently developed no-
reference image quality metric is adopted to rank the retrieved images from
the cloud. The image complexity from the free-energy-based brain theory and the
surface quality statistics in salient regions are collaboratively optimized to infer
the parameters. Experimental results confirm that the proposed technique can
efficiently create visually-pleasing enhanced images which are better than those
produced by the classical techniques in both subjective and objective
comparisons.
