A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

In this paper, a simple and efficient method, based on Quaternionic Distance Based Weber Descriptor (QDWD) and object cues, is proposed for saliency detection. Firstly, QDWD, which was initially designed for detecting outliers in color images, is used to represent the directional cues in an image. Meanwhile, two low-level priors, namely the color contrast and center cue of the image, are utilized and fused as an object-level cue. Finally, by combining QDWD with object cues, a reliable saliency map of the image can be computed. Experimental results, based on a widely used and openly available database, show that the proposed method is able to produce promising results, compared to other state-of-the-art saliency-detection models.