A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.
Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module
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abstract
In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions.
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Rethinking Image Histogram Matching for Image Classification
A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.