In recent years, multi-scale approach has been applied to image restoration tasks, including super-resolution, deblurring, etc., and has been proved beneficial to both optimization-based methods and learning-based methods to improve the restoration performance. Meanwhile, it is observed that high-frequency information plays an important role in blind motion deblurring. Unlike previous learning-based methods, which simply deepen deblurring network without discriminating the low-frequency contents and the high-frequency details, we propose a novel multi-scale convolutional neural network (CNN) framework with residual channel attention block (RCAB) for blind motion deblurring. RCAB has the residual in residual (RIR) structure, which consists of several residual groups with long skip connections and allows low-frequency information pass through the skip connections conveniently, and can adaptively learn more useful channel-wise features and pay more attention to high-frequency information. Experimental results show that our proposed method can obtain better deblurring images than state-of-the-art learning-based image deblurring methods in terms of both quantitative metrics and visual quality.
During model-based image dehazing, the role of the accuracy of transmission estimation is crucial, which has a
decisive effect on the final result. Considering that an ideal transmission map must be smooth, edge-preserving and free
of redundant false details, a fusion-based dark channel prior (DCP) dehazing algorithm is presented in this paper. On the
basis of DCP, a pixel-wise and a patch-wise transmission maps are obtained. Then an L0 smoothing filter and a large
scale Gaussian filter are applied to them respectively. Finally, a much more accurate refined transmission map is attained
through fusion and a haze-free image is restored using the atmosphere degradation model. Furthermore, a novel scheme
for setting the lower bound of transmission adaptively is also put forward. Experiments demonstrate a better and faster
dehazing capability over original DCP algorithm and state-of-the-art dehazing methods, especially in suppressing halo
artifacts, restoring details and coping with the haze existing in small-scale areas of depth discontinuity occluded by
foreground..
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