Dynamic Long-Short Range Structure Learning for Low-Illumination Remote Sensing Imagery HDR Reconstruction

A promising way for low-illumination (LI) remote sensing images high-dynamic range (HDR) reconstruction is to model the mapping function from the input LI images to the corresponding high-quality counterpart using deep convolution neural networks. Due to various image contents, the key for achieving pleasing performance lies on comprehensively exploit the image-specific long-rang (e.g., non-local similarity, low-rank) and short-range (e.g., local similarity, texture etc.) structures in the LI images using appropriate network architecture. However, most existing methods can only exploit either short-range or long-range structures that are contentagnostic shared across all images, thus limiting their generalization capacity. To tackle this problem, we propose a dynamic long-short range structure learning framework for LR remote sensing images HDR reconstruction. In contrast to existing methods, we introduce a novel two-branch network architecture including a pixel-aware dynamic module that can adaptively exploit the pixel-aware short-range structure surrounding each pixel depending on its feature representation, and a long-range transformer module that dynamically exploit the long-range correlation between image patchesin the deep feature space. Then, the learned long-short range structures are integrated and cast into pixel-wise scaling factors of an illumination enhance module to restore the LI image. It empowers us to effectively exploit the image-specific long-short range structures of each input IL images for accurate HDR reconstruction. Experimental results on remote sensing images with different levels of IL demonstrate the effectiveness of the proposed method.