Sharif Digital Repository

This paper proposes a novel fusion method for visible and infrared images. The infrared and visible samples are obtained by a sampling pattern such as star and spiral. Then, the samples are fused according to fusion rules. Finally, the proposed method is applied to fuse the infrared and visible samples. The proposed method is iterative and a significant advantage of it, besides its superior performance, is that it is faster than the previous compressive sensing based fusion methods. The simulation results confirm the success of the proposed method for fusion of infrared and visible images. © 2018 IEEE  

Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noise-corrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt- and-Pepper Noise (SPN) and Random-Valued Impulsive Noise (RVIN) removal from images and compare our results with other notable algorithms in the literature. Simulation indicates show that our algorithm is not only simple and fast, but also it outperforms the other... 

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The main contribution of our algorithm is its low complexity; it has much lower run-time than most other algorithms. The reconstruction quality of our algorithm is both objectively (in terms of PSNR and SSIM) and subjectively better or comparable to other state-of-the-art algorithms. We provide a cost function for our problem, present an iterative method to find its local minimum, and provide the analysis of the algorithm. As an application of this problem, we apply our algorithm for Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise...