Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising
This addresses nighttime image quality for intelligent surveillance systems, though it appears incremental as it builds on bio-inspired approaches with specific algorithmic modifications.
The paper tackles the problem of low-quality night images (low brightness, contrast, high noise) that hinder object detection in surveillance systems by proposing a bio-inspired enhancement algorithm that converts low-illuminance images to brighter, clearer ones without training sequences, showing advantages over existing methods like contrast pair, Meylan, and Retinex in experiments.
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.