Guided Infrared Image Super-Resolution via Cross-modal Progressive Guidance

Xinchen Ye, Siqi Wang, Rui Xu, Haojie Li

Abstract: Guided Infrared image Super-Resolution (GISR) aims to reconstruct low-resolution infrared images by leveraging high-resolution visible images that provide rich geometric and high-frequency details. The primary challenge lies in establishing cross-modal association to effectively extract and fuse complementary features while mitigating redundant information. Therefore, we propose a novel network named CPGNet, which integrates two key components: the Cross-modal Gating Module (CGM) and the Cross-modal Collaborative Module (CCM). CGM employs cross-gating mechanism combined with asymmetric convolutions to dynamically enhance the salient features from both modalities and filter out irrelevant information. Meanwhile, CCM utilizes spatial and channel collaborative importance mapping along with masking mechanism to effectively explore and combine relevant details from two modalities and generate guidance information for infrared reconstruction. Additionally, we design a hierarchical architecture for progressive guidance, which fuses infrared features with cross-modal guidance cues by progressively integrating guidance information. Extensive experiments on various datasets demonstrate the effectiveness of our proposed method.

External IDs:dblp:conf/mir/YeWXL25