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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: Jul 16, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

C²LNet: Fusing infrared and visible image via a cross-modality complementarity learning network.

Xingyu Sun¹, Xiaobin Liu¹, Jing Yuan¹

¹College of Artificial Intelligence, Nankai University, Tianjin, 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, 300350, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 14, 2026

Summary

This study introduces a new method for infrared and visible image fusion (IVIF) that explicitly mines and preserves complementary features between modalities. The Cross-modality Complementarity Learning Network (C²LNet) improves fusion performance and shows promise for industrial applications.

Keywords:

Attention learning Complementarity learning Fusing constraint Infrared and visible image fusion

Related Experiment Videos

Last Updated: Jul 16, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Infrared and visible image fusion (IVIF) is essential for combining information from different sensors.
Existing IVIF methods often fail to explicitly mine and preserve complementary features across modalities.
Implicit feature aggregation limits the effectiveness of current approaches.

Purpose of the Study:

To propose a novel network, C²LNet, for effective infrared and visible image fusion.
To explicitly mine and preserve cross-modality complementary features.
To enhance fused images for both visual quality and downstream semantic tasks.

Main Methods:

Developed a Cross-modality Complementarity Learning Network (C²LNet).
Introduced an Interactive Complementarity Mining (ICM) method to extract complementary features at various levels and regions.
Designed a Complementarity Fusing Constraint (CFC) to supervise training, ensuring feature embedding and enhancing discrimination.

Main Results:

C²LNet significantly outperforms state-of-the-art methods on benchmark datasets (RoadScene, M³FD, LLVIP).
Achieved notable improvements in the VIF metric: 3.2% on RoadScene, 6.0% on M³FD, and 4.6% on LLVIP.
Demonstrated strong generalization to real-world industrial scenarios, such as electricity factory inspections.

Conclusions:

C²LNet effectively addresses limitations in existing IVIF methods by explicitly mining and fusing complementary information.
The proposed method enhances fused image quality and discriminative power for semantic tasks.
C²LNet shows significant potential for practical engineering applications like autonomous robotic inspection.