Cosmin Ludusan

Multifocus image fusion and denoising: A variational approach

In this letter we propose a variational approach for concurrent image fusion and denoising of multifocus images, based on error estimation theory and Partial Differential Equations (PDEs). In real world scenarios the assumption that the inputs of an image fusion process contain only useful information, pertinent to the desired fused output, does not hold true more often than not. Thus, the image fusion problem needs to be addressed from a more complex, fusion-denoising point of view, in order to provide a fused result of greater quality. The novelty of our approach consists in defining an image geometry-driven, anisotropic fusion model, numerically expressed using an anisotropy-reinforcing discretization scheme that further increases the anisotropic behavior of the proposed fusion paradigm. The preliminary experimental analysis shows that robust anisotropic denoising can be attained in parallel with efficient image fusion, thus bringing two paramount image processing tasks into complete synergy. One immediate application of the proposed method is fusion of multifocus, noise-corrupted images.

Image enhancement using hybrid shock filters

We present a new approach based on Partial Differential Equations (PDEs) and shock filter theory for image enhancement in “Gaussian Blur (GB) + Additive White Gaussian Noise (AWGN)” scenarios. The main disadvantage of classic shock filters, inability of successfully filtering noisy images, is overcome by the introduction of a complex domain shock filter framework. Furthermore, the proposed method allows for better control and anisotropic, contour-driven shock filtering, via its control functions f1 and f2. The main advantages of our method consist in the ability of successfully enhancing GB+AWGN images while preserving a stable-convergent time behavior.

Coherence enhancing image sharpening and denoising using a novel shock filter model

We present a new approach based on Partial Differential Equations (PDEs) and shock filter theory for image enhancement in “Gaussian Blur (GB) + Additive White Gaussian Noise (AWGN)” scenarios. The main disadvantage of shock filters, inability of successfully filtering noisy images, is overcome by the introduction of a complex shock filter framework. The proposed method allows for better control and anisotropic shock filtering, via its control functions f1 and f2. The main advantages of our method consist in the ability of successfully enhancing GB+AWGN images while exhibiting a coherence enhancing behavior.