Due to the characteristics of water itself and the absorption and scattering of light by suspended particles in the water, a series of problems, such as low Signal-to-Noise Ratio (SNR) and low resolution, exist in underwater images. Most of the traditional processing methods include image enhancement, restoration and reconstruction rely on degradation model and have ill-posed algorithm problem. In order to further improve the effects and efficiency of underwater image restoration algorithm, an improved image super-resolution reconstruction method based on deep convolutional neural network was proposed. An Improved Dense Block structure (IDB) was introduced into the network of the method, which can effectively solve the gradient disappearance problem of deep convolutional neural network and improve the training speed at the same time. The network was used to train the underwater images before and after the degradation by registration and obtained the mapping relation between the low-resolution image and the high-resolution image. The experimental results show that on a self-built underwater image training set, the underwater image reconstructed by the deep convolutional neural network with IDB has the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) improved by 0.38 dB and 0.013 respectively, compared with SRCNN (an image Super-Resolution method using Conventional Neural Network) and proposed method can effectively improve the reconstruction quality of underwater images.
In order to improve the quality of medical image super-resolution reconstruction, a wide residual super-resolution neural network algorithm based on depthwise separable convolution was proposed. Firstly, the depthwise separable convolution was used to improve the residual block of the network, widen the channel of the convolution layer in the residual block, and pass more feature information into the activation function, making the shallow low-level image features in the network easier transmitted to the upper level, so that the quality of medical image super-resolution reconstruction was enhanced. Then, the network was trained by group normalization, the channel dimension of the convolutional layer was divided into groups, and the normalized mean and variance were calculated in each group, which made the network training process converge faster, and solved the difficulty of network training because the depthwise separable convolution widens the number of channels. Meanwhile, the network showed better performance. The experimental results show that compared with the traditional nearest neighbor interpolation, bicubic interpolation super-resolution algorithm and the super-resolution algorithm based on sparse expression, the medical image reconstructed by the proposed algorithm has richer texture detail and more realistic visual effects. Compared with the super-resolution algorithm based on convolutional neural network, the super-resolution neural network algorithm based on wide residual and the generative adversarial-network super-resolution algorithm, the proposed algorithm has a significant improvement in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity index).
Concerning that traditional vertical image feature extraction algorithms have poor effect on oblique image matching, a feature extraction algorithm, based on Accelerated KAZE (AKAZE) and Scale Invariant Feature Transform (SIFT) algorithm called AKAZE-SIFT was proposed. Firstly, in order to guarantee the accuracy and distinctiveness of image feature detection, AKAZE operator, which fully preserves the contour information of image, was utilized for feature detection. Secondly, the robust SIFT operator was used to improve the stability of feature description. Thirdly, the rough matching point pairs were determined by the Euclidean distance between object feature point vector and candidate feature point vectors. Finally, the homography constraint was applied to improve the matching purity by random sample consensus algorithm. To evaluate the performance of the feature extraction algorithm, the blur, rotation, brightness, viewpoint and scale changes under the condition of oblique photography were simulated. The experimental results show that compared with SIFT algorithm and AKAZE algorithm, the recall of AKAZE-SIFT is improved by 12.8% and 5.3% respectively, the precision of AKAZE-SIFT is increased by 6.5% and 6.1% respectively, the F1 measure of AKAZE-SIFT is elevated by 13.8% and 5.6% respectively and the efficiency of the proposed algorithm is higher than that of SIFT and slightly worse than that of AKAZE. For the excellent detection and description performance, AKAZE-SIFT algorithm is more suitable for oblique image feature extraction.