Image Noise Removing Using Semi-supervised Learning on Big Image Data

Impulse noise corruption in digital images frequently occurs because of errors generated in noisy sensors or communication channels, such as faulty memory locations in devices, malfunctioning pixels within the camera, and bit errors in transmission. Although the recently developed big data streaming enhances the viability of video communication, visual distortions in images that are caused by impulse noise corruption can negatively affect the viability of video communication applications. This paper develops a novel model that uses a devised cost function through semisupervised learning on a vast amount of corrupted image data with sparse labeled training samples to effectively remove the visual effects of impulse noise from these corrupted images. The experiments demonstrated that the proposed model significantly outperformed the existing state-of-the-art image reconstruction models when tested on a large image data set. To the best of our knowledge, this study is the first to specifically address the impulse noise removal problem for such large volumes of image data corrupted by high-density impulse noise.