13 research outputs found
Survey on CNN based super resolution methods
Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging
The deep learning model Transformer has achieved remarkable success in the
hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial
Self-Attention (SA) mechanisms. However, applying these designs to remote
sensing (RS) HSI restoration tasks, which involve far more spectrums than
typical HSI (e.g., ICVL dataset with 31 bands), presents challenges due to the
enormous computational complexity of using Spectral and Spatial SA mechanisms.
To address this problem, we proposed Hyper-Restormer, a lightweight and
effective Transformer-based architecture for RS HSI restoration. First, we
introduce a novel Lightweight Spectral-Spatial (LSS) Transformer Block that
utilizes both Spectral and Spatial SA to capture long-range dependencies of
input features map. Additionally, we employ a novel Lightweight
Locally-enhanced Feed-Forward Network (LLFF) to further enhance local context
information. Then, LSS Transformer Blocks construct a Single-stage Lightweight
Spectral-Spatial Transformer (SLSST) that cleverly utilizes the low-rank
property of RS HSI to decompose the feature maps into basis and abundance
components, enabling Spectral and Spatial SA with low computational cost.
Finally, the proposed Hyper-Restormer cascades several SLSSTs in a stepwise
manner to progressively enhance the quality of RS HSI restoration from coarse
to fine. Extensive experiments were conducted on various RS HSI restoration
tasks, including denoising, inpainting, and super-resolution, demonstrating
that the proposed Hyper-Restormer outperforms other state-of-the-art methods
Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images
(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.</p