Low-light image joint enhancement optimization algorithm based on frame accumulation and multi-scale Retinex

It is acknowledged that images taken under low-light conditions are easily affected by low visible light and noise, which can cause important image information loss, low signal-to-noise ratio, blurred edges, and poor subjective vision. Related researchers have targeted some solutions are proposed for the above problems, such as histogram equalization and gamma correction, but all have problems such as edge loss and color distortion. Based on the above problems, this paper proposes a low-light image enhancement optimization algorithm based on frame accumulation and multi-scale Retinex joint processing. First, single-channel image frame accumulation filtering is performed on the low-light image, and then the image is jointly enhanced with the optimized multi-scale Retinex algorithm. The experimental results show that the peak signal-to-noise ratio of the image processed by the joint enhancement optimization algorithm used in this article is increased to 51.2041 dB, which is 15.2633 dB higher than the original image and 1.799 dB higher than the image processed by the traditional MSRCR algorithm, structure similarity increased by 0.12, the enhanced image has higher grayscale resolution and signal-to-noise ratio, while retaining more image edges and detailed texture, reducing color distortion to a certain extent, and the generation of aperture artifacts is weakened. It has a high structural similarity to the original image. The overall quality of the image has been improved to a certain extent, and the subjective and objective evaluations are better than traditional algorithms. Finally, the comparison experiment verifies the effectiveness and practicability of the joint enhancement optimization algorithm in this paper to improve the low-light image quality, Which provides a new pre-processing method for future intelligent target detection, and has important research value.