Design of compensation algorithms for zero padding and its application to a patch based deep neural network
Year: 2024
Type: article
Abstract: In this article, compensation algorithms for zero padding are suggested to enhance the performance of deep convolutional neural networks. By considering the characteristics of convolving filters, the proposed methods efficiently compensate convolutional output errors due to zero padded inputs in a convolutional neural network. Primarily the algorithms are developed for patch based SRResNet for Single Image Super Resolution and the performance comparison is carried out using the SRResNet model but due to generalized nature of the padding algorithms its efficacy is tested in U-Net for Lung CT Image Segmentation. The proposed algorithms show better performance than the existing algorithm called partial convolution based padding (PCP), developed recently.
Source: PeerJ Computer Science
Authors: Safi Ullah, Seong-Ho Song
Institutions: Hallym University, University of Gujrat
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
Funder: Hallym University
OpenAlex ID: W4402011339