@Article{Cetinkaya2020a, author = {Cetinkaya, Ekrem and KIRAÇ, M. Furkan}, journal = {TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES}, title = {{Image denoising using deep convolutional autoencoder with feature pyramids}}, year = {2020}, issn = {1303-6203}, month = {7}, number = {4}, pages = {2096--2109}, volume = {28}, abstract = {Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary stepfor many computer vision applications. Various approaches have been used for image denoising throughout the yearsfrom spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-baseddiscriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieveflexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variantof feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along withcorrupted natural images are used during training to improve robustness against various noise levels. Experimental resultsshow that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly lesstraining time required compared to state of the art methods. Extensive experiments showed the proposed method givespromising performance in a wide range of noise levels with a single network.}, doi = {10.3906/elk-1911-138}, keywords = {Image denoising, convolutional autoencoder, feature pyramid, image processing}, url = {https://journals.tubitak.gov.tr/elektrik/issues/elk-20-28-4/elk-28-4-20-1911-138.pdf} }