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Depth maps are acquirable and irreplaceable geometric information that significantly enhances traditional color images. RGB and Depth (RGBD) images have been widely used in various image analysis applications, but they are still very limited due to challenges from different modalities and misalignment between color and depth. In this paper, a Fully Aligned Fusion Network (FAFNet) for RGBD semantic segmentation is presented. To improve cross-modality fusion, a new RGBD fusion block is proposed, features from color images and depth maps are first fused by an attention cross fusion module and then aligned by a semantic flow. A multi-layer structure is also designed to hierarchically utilize the RGBD fusion block, which not only eases issues of low resolution and noises for depth maps but also reduces the loss of semantic features in the upsampling process. Quantitative and qualitative evaluations on both the NYU-Depth V2 and the SUN RGB-D dataset demonstrate that the FAFNet model outperforms state-of-the-art RGBD semantic segmentation methods.

The development of deep learning provides a new way for solving the colorization problem on the grayscale image. Excellent coding-based methods appear in the automatic image colorization task, avoiding the unsaturated colour effect problem of previous methods based on the L2 loss function. Traditional neural networks come with high computational costs and a large number of parameters. Considering the limitation of memory and computing resources and aiming at lightweight, a novel grey image automatic colorization network is proposed. The basic idea of coding-based methods is used, regarding the colorization task as a pixel-level classification problem, meanwhile redesign and improve the colour encoding and decoding process. This network architecture leverages a lightweight convolution to reduce the computation and combines an efficient attention model to form a residual block as the kernel of the backbone network. Furthermore, an efficient image self-attention mechanism placed at the end of the network is applied to enhance the ultimate colouring results. The method proposed in this paper can maintain the natural colouring effect and significantly reduce the computational amount and network model parameters.

Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods.