Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Pan-sharpening involves integrating information from low-resolution multi-spectral and high-
resolution panchromatic images to generate high-resolution multi-spectral counterparts …

Global modeling-based image restoration frameworks have become popular. However, they
often require a high memory footprint and do not consider task-specific degradation. Our …

Pan-sharpening aims to generate high-resolution multi-spectral (MS) images by fusing PAN
images and low-resolution MS images. Despite its great advances, most existing pan …

Nighttime image dehazing is a challenging task due to the presence of multiple types of
adverse degrading effects including glow, haze, blur, noise, color distortion, and so on …

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-
scale modeling. However, spatial up-sampling operators (eg, interpolation, transposed …

As recognized, the ground-truth multispectral (MS) images possess the complementary
information (eg, high-frequency components) of low-resolution (LR) MS images, which can …

Quantitative remote sensing of crop diseases at the field or plot scale is essential for crop
management. Conventional approaches frequently rely solely on single-modal remote …

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …

Despite recent advancements in unified adverse weather removal methods, there remains a
significant challenge of achieving realistic fine-grained texture and reliable background …