An existing multi-scale residual network (MSRN) has demonstrated its success on conducting
the single image super-resolution (SISR) task. The MSRN consists of a number of multi-…

A new multi-scale deep learning (MDL) framework is proposed and exploited for conducting
image interpolation in this paper. The core of the framework is a seeding network that needs …

Existing multi-scale feature extraction methods extract image features using various convolution
window sizes conducted on the spatial dimension of the feature maps. However, such …

A novel image decomposition-guided network (IDGN) for image interpolation is proposed in
this paper by incorporating the fundamentals of subband image decomposition into the …

Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective
diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the …

The non-local attention mechanism has often been exploited in deep learning to capture
long-range dependencies (LRDs) from the same image for enhancing the performance of …

Anticorrosive behaviors of 2-mercaptobenzothiazole (MBT) self-assembled monolayers (SAMs)
on silver and zinc electrodes were comparatively studied by means of electrochemical …

Circular RNAs (circRNAs) have been found to be involved in cancer progression and
chemotherapy sensitivity. However, the biological function of circRNAs in triple-negative breast …

A novel content-based multi-scale network (CMNet) is proposed in this paper for conducting
single image super-resolution (SISR). Its core lies in a content-based multi-scale image …

A recently proposed quantization-table convolutional network (QCN) has proven as a state-of-the-art
JPEG image artifacts removal method. To reduce computational complexity, the …