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In this paper, we solve the problem of dynamic scenes deblurring with motion blur. Restoration of images in the presence of motion blur necessitates a network design that the receptive field can completely cover all areas that need to be deblurred, while the existing network increases the receptive field by continuously stacking the ordinary convolutional layer or increasing the size of the convolution kernel. However, these methods inevitably increase the computational burden of the network. We propose a novel architecture consisting of a channel adaptive residual module. Different features of the blurred image are extracted and distributed on each feature channel. Our network can calculate the weight of each channel through learning, and extract the image features adaptively according to different degrees of blurring and importance of information. We embed the module in a modified encoder-decoder design with skip connections to achieve multi-scale feature fusion for further performance improvement. The extensive comparison with the existing techniques in the baseline dynamic scene deblurring dataset shows that the proposed network can effectively realize image deblurring, and the accuracy and speed are comparable with the existing techniques.

There always exist contrast reduction and details degradation when the infrared imaging system works on water surface because of fog and water mist. An image enhancement method is proposed based on radiative transfer theory. Firstly, the infrared enhancement model is built according to radiative transfer theory, with which we construct an optimization frame. Secondly, edge matrix is designed for weight analysis, which is particularly important for next estimation of fine transmissivity and optical path radiation. Finally, enhancement is achieved with edge matrix. The main contribution and novelty includes the derivation of enhancement based on radiative transfer theory, design of edge matrix, and estimation of fine transmissivity and optical path radiation. The experimental comparisons show the proposed method can generate results with a good visual effect, which is proved by both subjective and objective assessment. The experiments also indicate that the proposed algorithm runs fast to apply in real system.INDEX TERMS Infrared enhancement, theory of radiative transfer, weight analysis, water surface imaging.

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