VEDA: Uneven light image enhancement via a vision-based exploratory data analysis model

TL;DR

The paper introduces VEDA, a simple yet effective vision-based model for uneven light image enhancement that avoids assumptions and priors, outperforming existing methods in natural scene processing.

Contribution

VEDA is a novel, assumption-free image enhancement model that decomposes images into contrast and residual components for multi-scale enhancement.

Findings

Achieves competitive enhancement results compared to state-of-the-art methods.

Does not rely on scene priors or iterative optimization.

Effectively balances noise suppression and detail preservation.

Abstract

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.