CNN-Based Classification of Degraded Images Without Sacrificing Clean Images

Abstract

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

Data augmentation technique for degraded images without losing the classification ability of clean images

“…The classification network may be fine-tuned with restored images. The knowledge distillation approach for degraded images 16 – 20 , 29 transfers the knowledge of a teacher network into a student network. Typically, the teacher network is a classification network trained with only clean images.…”

“…1. To overcome this drawback, Endo et al 18,20 introduced a network structure termed the feature adjustor. This paper proposes a data augmentation technique to overcome this drawback without relying on special network structures, in which degraded images are assumed to have a single known degradation with unknown levels of degradation.…”

“…Recently, the classification of degraded images has become an increasingly researched topic 9 – 19 A straightforward approach to address the classification of degraded images is to train a classification network using degraded images. Notably, even if these images include a single degradation, the degradation usually has various strengths of degradation.…”

“…1. To overcome this drawback, Endo et al 18 , 20 . introduced a network structure termed the feature adjustor.…”