| Single-frame methods | Methods on based local information | Background estimation | Median filtering;
Max-mean filtering;
Max-median filtering;
TDLMS adaptive filtering, etc
| From simple traditional filtering methods to adaptive filtering methods (such as TDLMS, etc.) |
| Morphological methods | New tophat, etc. | 1. Design more appropriate structural windows;
2. Use other information (such as local significance, local entropy, etc.) for weighting
|
| Directional derivative/ gradient methods | Directional derivative;
High order directional derivative;
Gradient, etc.
| 1. Obtain the directional derivative using facet kernel;
2. Introduce higher derivative;
3. Combined with other methods (such as contrast), etc.
|
| Local contrast | Difference-form LCM;
Ratio-form LCM;
Ratio-difference LCM, etc.
| 1. Transition from difference type or ratio type to ratio-difference joint type;
2. Improve the calculation window;
3. Use more information (such as local entropy, local signal to clutter ratio, etc.) as the weighting function;
4. Combined with other types of algorithms (such as frequency domain algorithm, background estimation algorithm, etc.)
|
| Methods based on nonlocal information | Frequency-domain methods | High pass filter;
Low pass filter;
Fourier transform;
Wavelet transform, etc.
| 1. Transition from simple Fourier transform and high/low pass filter to wavelet analysis;
2. Combined with other types of algorithms (such as morphological methods)
|
| Classifier | Clustering;
Support vector machine;
Neural network, etc.
| 1. Optimize classification parameters (such as distance threshold in clustering algorithm, mapping method in support vector machine, etc.);
2. Design a more suitable classification network (mainly for neural network method);
3. Combined with other types of algorithms (such as the combination of depth network and contrast algorithm)
|
| Over complete sparse representation | Sparse representation;
Group sparse representation, etc.
| 1. Construct an appropriate over complete target dictionary;
2. Construct the over complete dictionary for the target and background respectively;
3. Combined with other types of algorithms (such as frequency domain algorithm, etc.)
|
| Sparse and low-rank decomposition
(mainly refers to RPCA)
| RPCA for image matrix;
RPCA for image patch;
RPCA for tensor, etc.
| 1. Improve the data organization method (from image matrix to image patch or tensor);
2. Improve the relaxation conditions (from simple 1-norm, nuclear norm to more complex relaxation conditions);
3. Introduce regularization factors (such as total variation);
4. Improve the iterative solution process (such as protecting large singular values, or introducing weighting factors, etc.)
5. Combined with other types of algorithms (such as contrast algorithm, etc.)
|
| Multi-frame methods | Associated checking | Single-frame detection first, then correlation check between multi frames | Pipeline filtering;
Energy accumulation;
Markov theory;
Kalman filtering;
Particle filter, etc.
| From simple energy accumulation and time-domain verification to more complex verification theories and methods, such as Markov theory, Kalman filter, particle filter, and so on |
| Directed calculation | 3D search | 3D matched filter;
Dynamic programming, etc.
| From simple time-domain calculation (such as 3D matched filtering, dynamic programming, temporal variance, temporal difference, etc.) to spatial-temporal joint calculation (such as spatial-temporal contrast, spatial-temporal tensor, etc.) |
| 1D processing | Temporal variance;
Temporal power law;
Temporal inner product;
Temporal profile, etc.
|
| Inter-frame difference | Difference between two adjacent frames;
Difference between multi adjacent frames, etc.
|
| Spatial-temporal methods | Spatial-temporal local contrast;
Spatial-temporal tensor, etc.
|
