Low-complexity trainable denoising filter structure for image recognition applications

Abstract

Even in modern imaging equipment, different types of noise make their way into the images especially under bad lighting conditions and rough communication channels. While applying denoising filters, the preservation of the edge in images has always been an essential albeit a difficult task. This is particularly important for accuracy of further image processing tasks, such as recognition. We described a denoising approach to process noisy images specific for the task of classification with minimal impact on image resolution using low-complexity operations. Specifically, support vector machine-based machine learning classifiers have been proposed to dynamically select an appropriate filtering kernel while processing individual pixels in the input image. The selection is based on the image features in a local window. The provided experimental results on standard dataset prove that the proposed scheme performs as well as contemporary complex convolutional neural network (CNN)-based approaches for the task of denoising images before classification in the presence of a variety of mixed-type noise. Moreover, the proposed filtering approach can run at much higher speeds than their CNN-based counterparts while delivering similar or better performance. The proposed framework has been made publicly available as open-source code to facilitate further work by researchers and practitioners interested in this area.