Image Contrast Gain Control by Linear Neighbourhood Embedding
Jian Guan, Guoping Qiu · 2005
In this paper, we present a method that adaptively computes a contrast gain control map for the image through the use of a novel technique termed linear neighborhood embedding (LNE) which first computes a locally linear relation for each pixel and its neighbors and then embeds these relations globally in the gain map image. We borrow the “think globally fit locally ” concept and computational techniques from locally linear embedding (LLE) and compute the gain control image in closed forms by solving constrained optimization problems. We constrain the gain map locally following a gain contrast control mechanism similar to that found in the visual cortex to ensure that weak local contrasts are boosted and strong local contrasts are compressed, and propagate these local constraints globally following the original image pixels ’ locally linear relations. We have applied our technique to compress high dynamic range images for reproduction in low dynamic range media and to enhance ordinary digital photographs. Results demonstrate that our technique is capable of preserving local details while avoiding artifacts such as halo