Two-step modulus-based matrix splitting iteration methods for retinex problem

Chen, Fang; Zhu, Yu; Muratova, Galina V.

doi:10.1007/s11075-021-01103-y

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…According to Retinex theory [24], the illumination information of real scenes, such as halation, mainly exists in the low-frequency visible images, and the overall changes gently. Hence a low-frequency sequence generation model is constructed according to the low-frequency information in the luminance of the visible image and used to generate low-frequency components with different degrees of halation elimination.…”

Section: Low-frequency Sequence Generation Modelmentioning

confidence: 99%

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

2023

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The abuse of high beam lights dazzles the opposite drivers when the vehicles meet at night, which can easily cause traffic accidents. The existing night vision anti-halation algorithms based on different-source image fusion can eliminate halation and obtain fusion images with rich color and details. However, the algorithms mistakenly eliminate some high-brightness important information. In order to address the problem, a night vision anti-halation algorithm based on low-frequency sequence generation is proposed. The low-frequency sequence generation model is constructed to generate image sequences with different degrees of halation elimination. According to the estimated illuminance for image sequences, the proposed sequence synthesis based on visual information maximization assigns a large weight to the areas with good brightness so as to obtain the fusion image without halation and with rich details. In four typical halation scenes covering most cases of night driving, the proposed algorithm effectively eliminates halation while retaining useful high-brightness information and has better universality than the other seven advanced comparison algorithms. The experimental results show that the fusion image obtained by the proposed algorithm is more suitable for human visual perception and helps to improve night driving safety.

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Section: Low-frequency Sequence Generation Modelmentioning

confidence: 99%