Image Super-Resolution Through Compressive Sensing-based Recovery.

The primary aim of image super-resolution techniques is to produce a high resolution (HR) image from a low resolution (LR) image efficiently. Deep learning algorithms are being extensively used to address the ill-posed problem of single image super-resolution which requires extremely large data-sets and high processing power. When one does not have access to large data-sets or have limited processing power, an alternative technique may be in order.

A new preprocessing approach to improve the performance of CNN-based skin lesion classification.

Skin lesion is one of the severe diseases which in many cases endanger the lives of patients on a worldwide extent. Early detection of disease in dermoscopy images can significantly increase the survival rate. However, the accurate detection of disease is highly challenging due to the following reasons: e.

Increasing the quality of reconstructed signal in compressive sensing utilizing Kronecker technique.

Quality of reconstruction of signals sampled using compressive sensing (CS) algorithm depends on the compression factor and the length of the measurement. A simple method to pre-process data before reconstruction of compressively sampled signals using Kronecker technique that improves the quality of recovery is proposed. This technique reduces the mutual coherence between the projection matrix and the sparsifying basis, leading to improved reconstruction of the compressed signal.