Motivation: Medical image enhancement is a crucial part to improve the quality of the images. The excellent visual effects and image quality can help doctors make quick diagnoses. Among medical images, Magnetic Resonance Imaging (MRI) images play a vital role in clinical diagnosis. Its imaging principle highlights the human tissue part ignoring the boundary information sometimes. Moreover, some imaging results lose details in visual due to the low contrast and the quality of the images. To overcome these limitations, we propose an MRI enhancement method based on visual-attention by means of contrast adjustment and illumination component preservation. Description: The proposed framework includes image generation and image fusion to tackle the limitation of a single image. First, we assume an MRI image composed of tissues and details. We design an adaptive attenuation weight matrix based on the input MRI image according to a new definition of pixel energy. Then, an illumination-preserving image is introduced into the model for the attenuated image as compensation. Finally, an effective image fusion decision map calculation method is devised to create an enhanced MRI image with higher contrast and better perceptual quality. Results and conclusion: The experimental results show that it is a more effective enhancement method which has better performance on most of the objective evaluation metrics and stability than other 14 methods as well as maintains the balance between contrast and illumination of enhanced MRI images.

动机：医学图像增强是提高图像质量的关键部分。出色的视觉效果和图像质量可以帮助医生进行快速诊断。在医学图像中，磁共振成像（MRI）图像在临床诊断中起着至关重要的作用。它的成像原理突出了人体组织部分，有时会忽略边界信息。此外，由于低对比度和图像质量，某些成像结果会丢失视觉细节。为了克服这些局限性，我们提出了一种基于视觉注意的MRI增强方法，该方法通过对比度调整和照明分量保留来实现。说明：提出的框架包括图像生成和图像融合，以解决单个图像的局限性。首先，我们假设一个由组织和细节组成的MRI图像。我们根据像素能量的新定义，基于输入的MRI图像设计了一个自适应衰减权重矩阵。然后，将保存照明的图像引入到用于衰减图像的模型中作为补偿。最后，设计了一种有效的图像融合决策图计算方法，以创建具有更高对比度和更好感知质量的增强MRI图像。结果与结论：实验结果表明，与其他14种方法相比，它是一种更有效的增强方法，在大多数客观评估指标上具有更好的性能和稳定性，并且可以保持增强MRI图像的对比度和照度之间的平衡。将保存照明的图像引入到用于衰减图像的模型中作为补偿。最后，设计了一种有效的图像融合决策图计算方法，以创建具有更高对比度和更好感知质量的增强MRI图像。结果与结论：实验结果表明，与其他14种方法相比，它是一种更有效的增强方法，在大多数客观评估指标上具有更好的性能和稳定性，并且可以保持增强MRI图像的对比度和照度之间的平衡。将保存照明的图像引入到用于衰减图像的模型中作为补偿。最后，设计了一种有效的图像融合决策图计算方法，以创建具有更高对比度和更好感知质量的增强MRI图像。结果与结论：实验结果表明，与其他14种方法相比，它是一种更有效的增强方法，在大多数客观评估指标上具有更好的性能和稳定性，并且可以保持增强MRI图像的对比度和照度之间的平衡。