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المؤلفMalik, Junaid
المؤلفKiranyaz, Serkan
المؤلفYamac, Mehmet
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-09-24T08:57:19Z
تاريخ النشر2021
اسم المنشورProceedings - International Conference on Image Processing, ICIP
المصدرScopus
الرقم المعياري الدولي للكتاب2381-8549
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ICIP42928.2021.9506240
معرّف المصادر الموحدhttp://hdl.handle.net/10576/47891
الملخصDespite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to produce competitive results as compared to BM3D for AWGN image denoising. To this end, we configure networks with only two hidden layers and employ different neuron models and layer widths for comparing the performance with BM3D across different AWGN noise levels. Our results conclusively show that the recently proposed self-organized variant of operational neural networks based on a generative neuron model (Self-ONNs) is not only a better choice as compared to CNNs, but also provide competitive results as compared to BM3D and even significantly surpass it for high noise levels.
اللغةen
الناشرIEEE Computer Society
الموضوعDiscriminative learning
Image denoising
Operational neural networks
Self-organized operational neural networks
العنوانBM3D VS 2-LAYER ONN
النوعConference Paper
الصفحات1994-1998
رقم المجلد2021-September
dc.accessType Abstract Only


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