Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising

TL;DR

This paper introduces a bio-inspired night image enhancement method that improves brightness, contrast, and noise suppression without training data or recursive functions, outperforming existing algorithms in real and simulated tests.

Contribution

The proposed method uniquely combines contrast enhancement and denoising inspired by biological processes without requiring training sequences or recursive steps.

Findings

Significant improvement in brightness and contrast of night images.

Effective noise suppression demonstrated in experiments.

Outperforms contrast pair, Meylan, and Retinex algorithms.

Abstract

Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise. In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one. Different from existing bio-inspired algorithm, the proposed method doesn't use any training sequences, we depend on a novel chain of contrast enhancement and denoising algorithms without using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.