Single image super resolution based on multiscale local similarity and neighbor embedding

Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

Pairwise Operator Learning for Patch-Based Single-Image Super-Resolution

Motivated by the fact that image patches could be inherently represented by matrices, single-image super-resolution is treated as a problem of learning regression operators in a matrix space in this paper. The regression operators that map low-resolution image patches to high-resolution image patches are generally defined by the left and right multiplication operators. The pairwise operators are, respectively, used to extract the raw and column information of low-resolution image patches for recovering high-resolution estimations. The patch-based regression algorithm possesses three favorable properties. First, the proposed super-resolution algorithm is efficient during both training and testing, because image patches are treated as matrices. Second, the data storage requirement of the optimal pairwise operator is far less than most popular single-image super-resolution algorithms, because only two small sized matrices need to be stored. Last, the super-resolution performance is competitive with most popular single-image super-resolution algorithms, because both raw and column information of image patches is considered. Experimental results show the efficiency and effectiveness of the proposed patch-based single-image super-resolution algorithm.