Classification-Driven Dynamic Image Enhancement

By Vivek Sharma, Ali Diba, Davy Neven, Michael S. Brown, Luc Van Gool, Rainer Stiefelhagen. 2017

71 citations recorded by the source corpus.

Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer. In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception. To this end, we present a unified CNN architecture that uses a range of enhancement filters that can enhance image-specific details via end-to-end dynamic filter learning. We demonstrate the effectiveness of this strategy on four challenging benchmark datasets for fine-grained, object, scene, and texture classification: CUB-200-2011, PASCAL-VOC2007, MIT-Indoor, and DTD. Experiments using our proposed enhancement show promising results on all the datasets. In addition, our approach is capable of improving the performance of all generic CNN architectures.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020, methodology): “Following [47], all these recent works [46, 48, 49] utilize the same concept to learn a steerable filter [46], weather prediction filter [48], an image enhancement filter [49], and a dynamic motion motion representation filter [50] using source-target image pairs.”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, background): “Recently, deep learning has achieved great success in the field of low-level image processing [66] and nighttime scenes modeling [57] and understanding [18,62].”
DynamoNet: Dynamic Action and Motion Network (2019, methodology): “[21] where the authors propose transformation filters to do translation and rotation, all these papers [1, 24, 33] utilize the same concept (deep-down) to learn a steerable filter [1], weather prediction filter [24], or an enhancement filter [33] using input-output image pairs.”