Generating the decision map with accurate boundaries is the key to fusing multi-focus
images. In this paper, we introduce edge-preservation (EP) techniques into neural networks …

Medical image fusion plays a pivotal role in facilitating clinical diagnosis. However, the
quality of input medical images may be marred by noise, low contrast, and lack of …

In this paper, we proposes an image inpainting algorithm based on the modified Gray–Scott
(GS) model. We have added a fidelity term to control the edge and stability of pixel value …

Full-field optical angiography (FFOA) has considerable potential for clinical applications in
the prevention and diagnosis of various diseases. However, owing to the limited depth of …

Infrared imaging technology has attracted numerous interests in military, security,
transportation, etc. However, infrared images suffer from low contrast and blurry detail …

As an important multisensor data fusion technology, multifocus image fusion (MFIF)
integrates all focused information from source images to provide a comprehensive and …

Existing multi-focus image fusion (MFIF) methods often fail to preserve the uncertain
transition region and detect small focus areas within large defocused regions accurately. To …

In order to address driver's dazzle caused by the abuse of high beams when vehicles meet
at night, a night vision anti-halation algorithm based on image fusion combining visual …

Multi-focus-image-fusion (MFIF) is an approach of fusing two or more multi-focused images
into a single image that contains much more information than the previous input images. An …