  Uneven light image enhancement is a highly demanded task in many industrial
image processing applications. Many existing enhancement methods using physical
lighting models or deep-learning techniques often lead to unnatural results.
This is mainly because: 1) the assumptions and priors made by the physical
lighting model (PLM) based approaches are often violated in most natural
scenes, and 2) the training datasets or loss functions used by deep-learning
technique based methods cannot handle the various lighting scenarios in the
real world well. In this paper, we propose a novel vision-based exploratory
data analysis model (VEDA) for uneven light image enhancement. Our method is
conceptually simple yet effective. A given image is first decomposed into a
contrast image that preserves most of the perceptually important scene details,
and a residual image that preserves the lighting variations. After achieving
this decomposition at multiple scales using a retinal model that simulates the
neuron response to light, the enhanced result at each scale can be obtained by
manipulating the two images and recombining them. Then, a weighted averaging
strategy based on the residual image is designed to obtain the output image by
combining enhanced results at multiple scales. A similar weighting strategy can
also be leveraged to reconcile noise suppression and detail preservation.
Extensive experiments on different image datasets demonstrate that the proposed
method can achieve competitive results in its simplicity and effectiveness
compared with state-of-the-art methods. It does not require any explicit
assumptions and priors about the scene imaging process, nor iteratively solving
any optimization functions or any learning procedures.
