This paper presents a new approach of face recognition based on wavelet network using 2D fast wavelet transform and multiresolution analysis. This approach is divided in two stages: the training stage and the recognition stage. The first consists to approximate every training face image by a wavelet network. The second consists in recognition of a new test image by comparing it to all the training faces, the distances between this test face and all images from the training set are calculated in order to identify the searched person. The usual training algorithms presents some disadvantages when the weights of the wavelet network are computed by applying the back-propagation algorithm or by direct solution which requires computing an inversion of matrix, this computation may be intensive when the learning data is too large. We present in this paper our solutions to overcome these limitations. We propose a novel learning algorithm based on the 2D Fast Wavelet Transform. Furthermore, we have increased the performances of our algorithm by introducing the Levenberg–Marquardt method to optimize the learning functions and using the Beta wavelet which has at both an analytical expression and wavelet filter bank. Extensive empirical experiments are performed to compare the proposed method with other approaches as PCA, LDA, EBGM and RBF neural network using the ORL and FERET benchmarks.

A novel approach for face recognition based on fast learning algorithm and wavelet network theory

Hard exudates in retinal images are one of the most prevalent earliest signs of diabetic retinopathy. The accurate identification of hard exudates is of increasing importance in the early detection of diabetic retinopathy. In this paper, we present a novel method to identify hard exudates from digital retinal images. A feature combination based on stationary wavelet transform (SWT) and gray level co-occurrence matrix (GLCM) is used to characterize hard exudates candidates. An optimized support vector machine (SVM) with Gaussian radial basis function is employed as a classifier. A sample dataset consisting of 50 hard exudates candidates is used for identifying hard exudates. With the optimal SVM parameters, the classification accuracy of 84%, sensitivity of 88% and specificity of 80% are obtained.

Support vector machine based method for identifying hard exudates in retinal images

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross-validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel-level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X-ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.6380

M. P. Subramanian

Papers

Multi-resolution system for artifact removal and edge enhancement in computerized tomography images