Rhinitis is among the most common respiratory diseases in children. Nonallergic rhinitis, which involves nasal symptoms without evidence of systemic allergic inflammation or infection, is a heterogeneous entity with diverse manifestations and intensities. Nonallergic rhinitis accounts for 16%–89% of the chronic rhinitis cases, affecting 1%–50% (median 10%) of the total pediatric population. The clinical course of nonallergic rhinitis is generally rather mild and less likely to be associated with allergic comorbidities than allergic rhinitis. Here, we aimed to estimate the rate of coexisting comorbidities of nonallergic rhinitis. Nonallergic rhinitis is more prevalent during the first 2 years of life; however, its underestimation for children with atopic tendencies is likely due to low positive rates of specific allergic tests during early childhood. Local allergic rhinitis is a recently noted phenotype with rates similar to those in adults (median, 44%; range, 4%–67%), among patients previously diagnosed with nonallergic rhinitis. Idiopathic rhinitis, a subtype of nonallergic rhinitis, has been poorly studied in children, and its rates are known to be lower than those in adults. The prevalence of nonallergic rhinitis with eosinophilia syndrome is even lower. A correlation between nonallergic rhinitis and pollution has been suggested owing to the recent increase in nonallergic rhinitis rates in highly developing regions such as some Asian countries, but many aspects remain unknown. Conventional treatments include antihistamines, intranasal corticosteroids, and recent treatments include combination of intranasal corticosteroids with azelastin or decongestants. Here we review the prevalence, diagnosis, comorbidities, and treatment recommendations for nonallergic rhinitis versus allergic rhinitis in children.

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Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce noise-separable orthogonal transform features into a neural denoising framework. We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally. In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network. For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method. This can play a role of prior for learning a denoising process. It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks. INDEX TERMS Image denoising, deep learning for image denoising, orthogonal transform, multi-input network, PCA, wavelet transform.

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