Facial recognition has always gone through a consistent research area due to its non-modelling nature and its diverse applications. As a result, day-to-day activities are increasingly being carried out electronically rather than in pencil and paper. Today, computer vision is a comprehensive field that deals with a high level of programming by feeding the input images/videos to automatically perform tasks such as detection, recognition and classification. Even with deep learning techniques, they are better than the normal human visual system. In this article, we developed a facial recognition system based on the Local Binary Pattern Histogram (LBPH) method to treat the real-time recognition of the human face in the low and high-level images. We aspire to maximize the variation that is relevant to facial expression and open edges so to sort of encode edges in a very cheap way. These highly successful features are called the Local Binary Pattern Histogram (LBPH).
Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.
It is very interesting to reconstruct high‐resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super‐resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K‐singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low‐resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal‐to‐noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high‐resolution image. These parameters play an essential role in the reconstruction of the HR images.
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