In this paper, we propose an efficient single image super-resolution (SR) method for multi-scale image texture recovery, based on Deep Skip Connection and Multi-Deconvolution Network. Our proposed method focuses on enhancing the expression capability of the convolutional neural network, so as to significantly improve the accuracy of the reconstructed higher-resolution texture details in images. The use of deep skip connection (DSC) can make full use of low-level information with the rich deep features. The multi-deconvolution layers (MDL) introduced can decrease the feature dimension, so this can reduce the computation required, caused by deepening the number of layers. All these features can reconstruct high-quality SR images. Experiment results show that our proposed method achieves state-of-the- art performance.
Characteristics of an image, such as smoothness, edge, and texture, can be better preserved using the nonlocal differential operator in image processing. We establish an L1-based nonlocal total variational (NLTVL1) model based on Retinex theory that can be solved by a fast computational algorithm via the alternating direction method of multipliers. Experiential results demonstrate that our NLTVL1 method has a good performance on enhancing contrast, eliminating the influence of nonuniform illumination, and suppressing noise. Furthermore, compared with previous works, including traditional Retinex methods and variational Retinex methods, our proposed approach achieves superior performance on edge and texture preservation and needs fewer iterations on recovering the reflectance image, which is illustrated by examples and statistics.
It is common that textures occur in real-word color image, moreover, textures could cause difficulties in image segmentation. For the purpose of solving those difficulties, we put forward a new model. In this model we only need the structural and oscillating components’ information of the real color image. This model is based on the VO model, MTV and active contour models. We will use the fast Split Bregman algorithm to solve this model. The results of our model is mentioned in numerical experiments.
Frame difference method is a good method for motion segmentation, but its result contains much wrong motion regions and incomplete motion objects. In this paper we combine variational method with frame difference method to propose two motion segmentation models, and the proposed models are based on different invariance assumptions. The models can detect motion objects and make up for the inadequacy of frame differential method with smooth terms. Experimental results show that the proposed models can detect motion objects better.
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