Generalized Imaging Augmentation via Linear Optimization of Neurons

Daoyu Li (Beijing Institute of Technology), Lu Li (Beijing Institute of Technology), Bin Li (Beijing University of Posts and Telecommunications), Liheng Bian (Beijing Institute of Technology)*
The 34th British Machine Vision Conference


Most deep networks for computational photography tasks require large-scale training, which is time-consuming, computing-cost, and even hard to implement for certain data-unaccessible tasks. The emerging untrained convolutional networks (CNNs) rely on explicit physical models whose discrepancies and disturbances would lead to unsatisfactory performance. In response to these challenges, this work reports a generalized augmenting technique for computational photography techniques based on linear optimization of neurons (LION). LION linearly transforms the neurons of a pre-trained CNN and optimizes the transformation coefficients using a model-free color and texture regularization. Leveraging the inherent representation capabilities of the deep feature domain, we can enhance the quality of output images through a simple linear transformation of the pre-trained network features, without modifying network parameters or architecture. Furthermore, inspired by the concept of deep image prior, we develop a generalized workflow based on LION for augmenting untrained networks and conventional methods. A series of experiments have validated the technique's effectiveness for general imaging augmentation in underwater, low-light, and computational lensless imaging applications.



author    = {Daoyu Li and Lu Li and Bin Li and Liheng Bian},
title     = {Generalized Imaging Augmentation via Linear Optimization of Neurons},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {}

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