Multifocus image fusion based on robust principal component analysis

Multi-focus image fusion: A Survey of the state of the art

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have...

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Fractional differential and variational method for image fusion and super-resolution

In the nonsubsampled contourlet transform (NSCT) domain, a novel image fusion algorithm based on the visual attention model and pulse coupled neural networks (PCNNs) is proposed. For the fusion of high-pass subbands in NSCT domain, a saliency-motivated PCNN model is proposed. The main idea is that high-pass subband coefficients are combined with their visual saliency maps as input to motivate PCNN. Coefficients with large firing times are employed as the fused high-pass subband coefficients. Low-pass subband coefficients are merged to develop a weighted fusion rule based on firing times of PCNN. The fused image contains abundant detailed contents from source images and preserves effectively the saliency structure while enhancing the image contrast. The algorithm can preserve the completeness and the sharpness of object regions. The fused image is more natural and can satisfy the requirement of human visual system (HVS). Experiments demonstrate that the proposed algorithm yields better performance.

https://downloads.hindawi.com/journals/mpe/2013/135182.pdf

Image Fusion Based on Nonsubsampled Contourlet Transform and Saliency-Motivated Pulse Coupled Neural Networks

Multifocus image fusion and denoising: A variational approach

We present a new approach based on Partial Differential Equations (PDEs) for image enhancement in generalized "Gaussian Blur (GB) + Additive White Gaussian Noise (AWGN)" scenarios. The inability of the classic shock filter to successfully process noisy images is overcome by the introduction of a complex 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.

/pdf/morphological-sharpening-and-denoising-using-a-novel-shock-hk3ke1ymbu.pdf

Morphological sharpening and denoising using a novel shock filter model

We are proposing a new filter for image restoration and enhancement tasks that relies on image orientation analysis techniques for steering and directing smoothing or enhancement processes. The filter is developed under the Partial Differential Equations (PDE) framework using asymmetric orientation estimation operators and it has better junction preservation and noise removal properties than existing classical PDE based methods. We employ an experimental setup involving several computer generated images and a statistical interpretation of the results for proving the efficiency of the method in processing images composed of directional textures and degraded by additive Gaussian noise. The algorithm is compared also with state-of-the-art non-PDE based methods in filtering images containing oriented patterns.

A novel diffusion filter for image restoration and enhancement

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 f 1 and f 2 . The main advantages of our method consist in the ability of successfully enhancing GB+AWGN images while preserving a stable-convergent time behavior.

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 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 f 1 and f 2  The main advantages of our method consist in the ability of successfully enhancing GB+AWGN images while exhibiting a coherence enhancing behavior

Cosmin Ludusan

Papers

Multifocus image fusion and denoising: A variational approach

Morphological sharpening and denoising using a novel shock filter model

A novel diffusion filter for image restoration and enhancement

Image enhancement using hybrid shock filters

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