A joint framework for underwater sequence images stitching based on deep neural network convolutional neural network

Panoramic stitching technology provides an effective solution for expanding visual detection range of the autonomous underwater vehicle. However, absorption and scattering of light in the water seriously deteriorate the underwater imaging in terms of distance and quality, especially the scattering sharply decreases the underwater image contrast and results in serious blur. This reduces the number of matching feature points between the underwater images to be stitched, while fewer matched points generated make image registration and stitching difficult. To solve the problem, a joint framework is established, which firstly involves a convolutional neural network-like algorithm composed of a symmetric convolution and deconvolution framework for underwater image enhancement. Then, it proposes an improved convolutional neural network-random sample consensus method based on VGGNet-16 framework to generate more correct matching feature points for image registration. The fusion method based on Laplacian pyramid is applied to eliminate artificial stitching traces and correct the position of stitching seam. Experimental results indicate that the proposed framework can restore the color and detail information of underwater images and generate more effective and sufficient matching feature points for underwater sequence images stitching.