A HVS-inspired Attention Map to Improve CNN-based Perceptual Losses for Image Restoration
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Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and our visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on underlying spatial frequency where the visual system might be most sensitive to distortions. Based on this prior, we design an attention map that is applied to feature maps in the perceptual loss, helping it to identify regions that are of more perceptual importance. The results will demonstrate that the proposed technique helps improving the correlation of the perceptual loss with human subjective assessment of perceptual quality and also results in a loss which delivers a better perception-distortion trade-off compared to the widely used perceptual loss in CNN based image restoration problems.
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