Brain MRT image super resolution using discrete cosine transform and convolutional neural network

High Resolution (HR) images have numerous applications, such as video conferencing, remote sensing, medical imaging, etc. Furthermore, a few challenges with the super resolution algorithms of magnetic resonance brain images are now obtainable, namely, low sensitivity, significant frequency noise as well as poor resolution. To fix these problems, a Convolutional Neural Network (CNN) based Discrete Cosine Transform (DCT) singular frame quality improvement method is described. There are two stages in this proposed method, involving training and testing. During the training stage, the HR, and Low Resolution (LR) pictures are employed as input, and they are preprocessed to create blocks of images. The histogram and DCT are used for extracting the features from the LR and HR blocks, and these extracted features are assigned with class id. The CNN, which extracts the features and allocates class id, receives its feature extractor as its final input. An LR input image is once more divided into [2 × 2] blocks during the testing stage, so each block histogram and DCT feature are estimated. Each feature vector is fed into the neural network as well as the results are contrasted with a set of feature vectors that have been recorded, in addition to the class id that has been allocated to a certain vector. In order to generate a Super resolution image with an LR image, a relevant HR block is then swapped out for this LR block. These results indicated that the initial dataset can achieve 22.4 and 19.5 Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) values while measuring the effectiveness of this proposed method using RMSE and PSNR. Then, the second dataset illustrates that the PSNR and RMSE values are 20.1 and 25.5. For the third dataset, the values are 45.7 and 12.3, respectively. However, the presented method works better than the neural method of Super Resolution Channel Spatial Modulation Network and resolution enhancement technique

Estimation and Management of Performance Limiting Factors in the Development of 1 kW Peak Power Pulsed Fiber MOPA at 1550 nm

An all-fiber three-stage master oscillator power amplifier (MOPA), based on Erbium and Erbium-Ytterbium co-doped fibers, has been designed and developed. The performance of such a laser is primarily limited by amplified spontaneous emission (ASE), Yb bottlenecking, and non-linear effects. Other important factors, that need to be considered towards performance improvement, are fiber bend diameter and heat generated in the fiber. This paper describes the methodology for the estimation and management of these limiting factors for each amplifier stage. The work presented here is limited to the fibers which are commercially easily available, unlike customised Yb- free large mode area (LMA) Erbium-doped fibers, where very high peak and average powers are being reported due to the absence of Yb ASE. Presented experimental results and discussion shall be beneficial for the fiber laser amplifier designers. With suitable management, 1 kW peak power pulses of 30 ns duration at 200 kHz repetition rate have been achieved with 30 % optical efficiency. The collimated output of 6 W average power (limited by Yb ASE) with high beam quality (M2 ≈ 1.6) at 1550 nm can be employed for a variety of applications. By adding additional amplifier stages, power can be scaled further