LCSCNet Linear Compressing-Based Skip-Connecting Network for Image Super-Resolution


For image super-resolution, we present in this research a short but effective network design dubbed the linear compressing based skip-connector network (LCSCNet). A linear compression layer is created in LCSCNet for skip connections, which joins previous feature maps and differentiates them from newly investigated feature maps, in comparison to two sample network architectures with skip connections, ResNet and DenseNet. In this way, the suggested LCSCNet has the advantages of DenseNet's differentiate feature treatment and ResNet's parameter-economic form. As a result, we also present an adaptive element-wise fusion technique with multi-supervised training in order to better use hierarchical information from both low and high levels of diverse receptive fields of deep models. The usefulness of LCSCNet has been demonstrated experimentally and in comparison with leading-edge algorithms.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation Radiosurgery Application ABSTRACT: We conduct an in-depth analysis of a Deep Learning model by using it to segment three-dimensional
PROJECT TITLE : On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks ABSTRACT: To fully realize the potential of deep learning in histopathology applications, a bottleneck
PROJECT TITLE : Multi-Magnification Image Search in Digital Pathology ABSTRACT: This study proposes the use of multi-magnification image representation and investigates the effect that magnification has on content-based image
PROJECT TITLE : Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation ABSTRACT: The long-term goal of image restoration and manipulation is to acquire a solid understanding of image priors. Existing
PROJECT TITLE : Learning Deformable Image Registration from Optimization Perspective, Modules, Bilevel Training and Beyond ABSTRACT: The goal of conventional deformable registration methods is to solve an optimization model that

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry