Medium spatial resolution satellite image series is important for some applications. Currently, the most widely used medium resolution data is from Sentinel-2 and Landsat-8 satellites. For the purpose of acquiring dense image series, the fusion of them has drawn much attention. In this field, the deep learning-based fusion models have appeared excellent performance, which demonstrated the applicability for deep learning for the fusion. However, existing deep learning based fusion models all need high quality and abundant training data, which may limit their applications. To address this problem, we develop an unsupervised fusion model based on convolutional neural networks (CNNs), which can fuse the Sentinel-2 and Landsat images without other training data. This fusion model takes full advantage of CNNs in the image fusion task, but get raid of the limitation of the requirement of training data. In the experiment, we test the new model using the Sentinel-Landsat dataset, and compare it with other two fusion approaches. The experimental results indicate that the proposed fusion model has remarkable fusion performance especially in terms of accuracy. However, it is can not capture the surface changes well, which is the main direction for us to improve it in the future.

High-resolution remote-sensing-image change detection is widely used in urban dynamic monitoring, geographic information updating, natural disaster monitoring, illegal building investigation, and land resource surveys. Common change-detection algorithms are mainly implemented in a fully supervised manner that relies on a large number of high-quality samples. Compared with a building change-detection dataset, a building semantic-segmentation dataset is easier to accumulate and obtain. Making full use of this semantic information in the design of a building change-detection network can effectively reduce the sample size required to train a change-detection model. In view of this, a semantic feature-guided Siamese change-detection framework is devised in this letter. The framework effectively exploits the prior information of building semantic features and uses the popular transformer structure to improve the change analysis module. The results of extensive experiments on two public datasets show that the framework is more accurate than the other state-of-the-art change detection algorithms and can effectively reduce the dependence of data on change detection samples in the model training process.

This paper proposes aninteractiveapproach usingjoint image-noise filteringfor achieving high quality image-noise separation. The core of the system is our novel joint image-noise filter which operates in both image and noise domain, and can effectively separate noise from both high and low frequency image structures. A novel user interface is introduced, which allows the user to interact with both the image and the noise layer, and apply the filter adaptively and locally to achieve optimal results. A comprehensive and quantitative evaluation shows that our interactive system can significantly improve the initial image-noise separation results. Our system can also be deployed in various noise-consistent image editing tasks, where preserving the noise characteristics inherent in the input image is a desired feature.

This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. The robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. The deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. The results are shown to be significantly better than those of previous approaches.

This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adaptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of our algorithm compared with previous single frame and multi-frame algorithms.