Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (3): 36-42.doi: 10.16180/j.cnki.issn1007-7820.2021.03.007

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Digital Image Composition Optimization Based on Salient Feature Algorithm

SHAO Hang,WANG Yongxiong,QIN Yulong   

  1. School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-12-12 Online:2021-03-15 Published:2021-03-10
  • Supported by:
    National Natural Science Foundation of China(61673276);National Natural Science Foundation of China(61703277)


Image composition is an important factor in the digital image aesthetic quality. Existing computer optimization algorithms have a number of shortcomings, such as visual imbalance and poor integrity. Therefore, this paper proposes a novel composition optimization algorithm based on the deep learning model. This algorithm combines salient features with improved visual balance to achieve composition optimization that is more in line with image aesthetics. The deep convolutional neural network is based on VGG-16, weighting two loss functions and averaging image pixel values. After training, the network could achieve end-to-end saliency regression without any pre-processing or post-processing. Comparative experiments and quantitative verification results demonstrate that compared with the current traditional optimization approaches, the proposed algorithm has obvious advantages in improving visual balance. The processed image has a significantly improved sense of balance, which is more in line with the principles of aesthetic evaluation and visual perception.

Key words: image aesthetic, composition optimization, deep learning, salient feature, end-to-end model, visual balance

CLC Number: 

  • TP391