Explore MATLAB source code curated for "计算量" with clean implementations, documentation, and examples.
This algorithm implements Direction of Arrival (DOA) estimation using maximum likelihood principles, distinguishing itself from subspace-based DOA algorithms. It requires multi-dimensional search operations, resulting in higher computational complexity but potentially offering superior estimation accuracy.
The Harris algorithm is a corner detection method that identifies invariant feature points in images, significantly reducing computational load and accelerating processing speed. However, this approach leads to substantial information loss. The RANSAC (Random Sample Consensus) algorithm calculates mathematical models from point sets, effectively eliminating mismatched Harris corners to produce more authentic and accurate matching results.
This algorithm presents an improved version of the K-SVD algorithm, designed specifically for sparse dictionary learning. It effectively reduces computational complexity and accelerates dictionary update speed compared to traditional methods. The implementation typically involves optimized sparse coding steps and efficient singular value decomposition (SVD) operations.
This algorithm implements high-precision frequency estimation through continuous FFT spectrum refinement, achieving accuracy up to two decimal places. Compared to MUSIC and ESPRIT methods, it significantly reduces computational complexity while maintaining exceptional precision through iterative peak localization and interpolation techniques.
Fast algorithm for Fractional Fourier Transform based on convolution principles, with computational complexity approaching that of FFT
This paper introduces an innovative image denoising method that demonstrates dual advantages over existing algorithms: reduced computational complexity and superior restoration quality. The implementation utilizes optimized filtering techniques with potential integration of deep learning architectures for enhanced performance.