Super Resolution of Remote Sensing Images Using Edge-Directed Radial Basis Functions

Edge-Directed Radial Basis Functions (EDRBF) are used to compute super resolution(SR) image from a given set of low resolution (LR) images differing in subpixel shifts. The algorithm is tested on remote sensing images and compared for accuracy with other well-known algorithms such as Iterative Back Projection (IBP), Maximum Likelihood (ML) algorithm, interpolation of scattered points using Nearest Neighbor (NN) and Inversed Distance Weighted (IDW) interpolation, and Radial Basis Functin(RBF) . The accuracy of SR depends on various factors besides the algorithm (i) number of subpixel shifted LR images (ii) accuracy with which the LR shifts are estimated by registration algorithms (iii) and the targeted spatial resolution of SR. In our studies, the accuracy of EDRBF is compared with other algorithms keeping these factors constant. The algorithm has two steps: i) registration of low resolution images and (ii) estimating the pixels in High Resolution (HR) grid using EDRBF. Experiments are conducted by simulating LR images from a input HR image with different sub-pixel shifts. The reconstructed SR image is compared with input HR image to measure the accuracy of the algorithm using sum of squared errors (SSE). The algorithm has outperformed all of the algorithms mentioned above. The algorithm is robust and is not overly sensitive to the registration inaccuracies

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM

A Block-Based Regularized Approach for Image Interpolation

This paper presents a new efficient algorithm for image interpolation based on regularization theory. To render a high-resolution (HR) image from a low-resolution (LR) image, classical interpolation techniques estimate the missing pixels from the surrounding pixels based on a pixel-by-pixel basis. In contrast, the proposed approach formulates the interpolation problem into the optimization of a cost function. The proposed cost function consists of a data fidelity term and regularization functional. The closed-form solution to the optimization problem is derived using the framework of constrained least squares minimization, by incorporating Kronecker product and singular value decomposition (SVD) to reduce the computational cost of the algorithm. The effect of regularization on the interpolation results is analyzed, and an adaptive strategy is proposed for selecting the regularization parameter. Experimental results show that the proposed approach is able to reconstruct high-fidelity HR images, while suppressing artifacts such as edge distortion and blurring, to produce superior interpolation results to that of conventional image interpolation techniques

DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Recently, convolutional neural networks (CNNs) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution (SR) from multitemporal unregistered imagery have received little attention so far. This article proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire SR process relies on a single CNN with three main stages: shared 2-D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3-D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images. The method presented in this article is the winner of the PROBA-V SR challenge issued by the European Space Agency (ESA)

IEEE Transactions On Circuits And Systems For Video Technology: Vol. 23, No. 8, August 2013

1. Edge-Directed Single-Image Super-Resolution via Adaptive Gradient Magnitude Self-interpolation / L. Wang, S. Xiang, G. Meng, H. Wu, C. Pan 2. Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm / C. R. del Blanco, F. Jaureguizar, N. Garcia 3. High Performance and Hardware Efficient Multiview Video Coding Frame Scheduling Algorithms and Architectures / M. Choi, L. J. Chang, J. Kim 4. Constructing a No-Reference H.264/AVC Bitsream-based Video Quality Metric Using Genetic Programming-based Symbolic Regression / N. Staelens, D. Deschrijver, E. Vladislavleva, B. Vermeulen, T. Dhaene, P. Demeester 5. Dependent Joint Bit Allocation for H.264/AVC Statistical Multiplexing Using Convex Relaxation / C. Pang, O. C. Au, J. Dai, F. Zou 6. Foreground Estimation Based on Linear Regression Model With Fused Sparsity on Outliners / G. Xue, L. Song, J. Sun 7. Heterogenous Visual Codebook Integration via Consensus Clustering for Visual Categorization / R. J. Lopez-Sastre, J. Renes-Olalla, P. Gil-Jimenez, S. Maldonado-Bascon, S. Lafuente-Arroyo 8. Hardware Implementation of a Fast and Efficient Haze Removal Method / Y. -H. Shiau, H. -Y. Yang, P. -Y. Chen, Y. -Z. Chuang 9. Background Modeling Through Statistical Edge-Segment Distributions / A. R. Rivera, M. Murshed, J. Kim, O. Chae 10. Geometric Bargaining Approach for Optimizing Resources Allocation in Wireless Visual Sensor Networks / K. Pandremmenou, L. P. Kondi, K. E. Parsopoulos 11. Evaluation and FPGA Implementation of Sparse Linear Solvers for Video Processing Applications / P. Greisen, M. Runo, P. Guillet, S. Heinzle, A. Smolic, H. Kaeslin, M. Gross 12. Visual Importance- and Discomfort Region Selective Low-Pass Filtering for Reducing Visual Discomfort in Stereoscopic Displays / Y. J. Jung, H. Son, S. -il Lee, F. Speranza, Y. M. Ro Etc