Explore MATLAB source code curated for "红外图像" with clean implementations, documentation, and examples.
This paper proposes a dual-plateau histogram equalization algorithm specifically designed to enhance infrared images, particularly for improving the visibility of low-contrast small targets. The implementation involves preprocessing, dual-platform histogram equalization, and weighted averaging to boost image contrast.
Implementation of infrared and visible image fusion based on 2-level wavelet decomposition with multiple fusion rules for enhanced image quality
Implementation of infrared image non-uniformity correction through histogram statistics and processing techniques, with special focus on addressing stripe non-uniformity commonly found in infrared imagery using algorithmic approaches.
This program implements infrared and visible image registration by leveraging edge information from both image types. The implementation performs edge detection to identify closed contours, then utilizes chain code representation of contours for precise image alignment. Key processes include Canny edge detection, contour extraction algorithms, and chain code-based feature matching.
Implementation of RLS algorithm based on quadratic polynomial for correcting non-uniformity noise and effects in infrared imagery, featuring adaptive parameter tuning and real-time processing capabilities.
MATLAB source code implementing non-uniformity correction for infrared images, with verified error-free performance and detailed algorithmic descriptions
This MATLAB implementation demonstrates the use of neural network backpropagation method to train the correspondence relationship between infrared image pixel values and temperature, as presented in a research paper.
Simulation of non-uniformity noise in images based on principles of infrared image non-uniformity, with code implementation details for realistic noise modeling
Implementation of infrared image non-uniformity correction using the two-point method. Absolutely correct and practical with code-ready algorithm explanation.
Wavelet-based image fusion involves merging complementary information from multi-sensor data to create a new composite image, primarily for applications like target surveillance and recognition. This paper proposes an image fusion method utilizing wavelet transform and analyzes the fusion results of visible-light and infrared images. The implementation demonstrates excellent fusion effects with clear distinction between targets and backgrounds, while maintaining smooth edge transitions. The method shows promising application prospects through its algorithmic efficiency and robustness.