Abstract: Aiming at  the problem of texture detail loss in common  image enhancement techniques when improving the contrast of low-quality images, we proposed a low-quality image enhancement method based on dynamical system by constructing a four-dimensional feedforward neural network model and optimizing the output function. Firstly, through the analysis of the dynamical characteristics of the neural network model, we studied the  parameter combinations that achieved optimal signal amplification effects. Secondly, comparative experiments with mainstream enhancement algorithms were conducted on high-complexity medical image datasets. The results show that this method can enhance low-quality images with problems such as detail loss, brightness reduction, and noise contamination into high-quality images, and is  suitable for medical image enhancement processing with stringent quality requirements. The proposed method  provides a new technical approach for fields with strict quality requirements, such as medical images, effectively balancing image contrast enhancement and detail preservation, and  improving the usability of low-quality images in practical applications such as clinical diagnosis.

Key words: image enhancement,  , neural network model, low-quality image,  , feedforward neural network