Intensified charge-coupled device (ICCD) images are captured by ICCD sensors in extremely low-light conditions. They often contains spatially clustered noises and general filtering methods do not work well. We find that the scale of the clustered noise in ICCD sensing images is often much smaller than that of the true structural information. Then the clustered noise can be identified by properl...

Despite the enormous performance of deep neural networks (DNNs), recent studies have shown their vulnerability to adversarial examples (AEs), i.e., carefully perturbed inputs designed fool targeted DNN. Currently, literature is rich with many effective attacks craft such AEs. Meanwhile, defense strategies been developed mitigate this vulnerability. However, these latter showed effectiveness aga...

Recent developments in computational imaging and restoration have heralded the arrival and convergence of several powerful methods for adaptive processing of multidimensional data. Examples include Moving Least Square (from Graphics), the Bilateral Filter and Anisotropic Diffusion (from Machine Vision), Boosting and Spectral Methods (from Machine Learning), Non-local Means (from Signal Processi...

Recent developments in computational imaging and restoration have heralded the arrival and convergence of several powerful methods for adaptive processing of multidimensional data. Examples include Moving Least Square (from Graphics), the Bilateral Filter and Anisotropic Diffusion (from Machine Vision), Boosting, Kernel, and Spectral Methods (from Machine Learning), Non-local Means and its vari...

Most advancement in image de-noising algorithms are involving relatively poor numbers of patches and exploiting its similarity. These patch-based methods are completely based on matching of patches and their performance is restricted by the ability to dependably find suitably parallel patches. As number of patches grows, studies show that a point of retreating returns is reached where the perfo...

Wavelet theory has been extensively developed in the function space L2 and discrete wavelet transform has successful applications in many areas. However, to understand better the performance of different discrete wavelet transforms, it is important to investigate their underlying discrete wavelet systems in l2. Though some preliminary results have been found recently, despite the fact that stab...

Image denoising is a classic but still important issue in image processing as the effect has significant impact on subsequent results, such target recognition and edge detection. In past few decades, various methods have been proposed, model-based learning-based methods, they achieved promising results. However, no stand-alone method consistently outperforms others different complex imaging sit...

Cryo-Electron Tomography is a leading imaging technique in structural biology, which is capable of acquiring two-dimensional projections of cellular structures at high resolution and close-to-native state. Due to the limited electron dose the resulting projections exhibit extremely low SNR and contrast. The 3D structure is then reconstructed and passed through a number of post-processing steps ...

Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only...