A fast implementation of Adaptive Histogram Equalization is given in this paper
A Fast Implementation Of Adaptive Histogram Equalization
2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4, no. null (2006): 1330-+
Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement. But it's quite computationally expensive and time consuming. In this paper, a fast implementation of AHE based on pure software techniques is proposed Three accelerative techniques are combined to form the new fast AHE: First, local ...更多
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- Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement.
- From Algorithm 1, the authors could find that AHE is quite computationally expensive
- For every pixel, it need W2 additions to get the local histogram, and l additions for CHistl, one multiplication and one division to map the origin grey level to new one.
- Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement
- Since local histogram equalization must be performed for every pixel in the entire image, the computation complexity is very high
- (2) In AHE, the new grey level is depending on the cumulative histogram function value in origin grey level
- As we can see from the table, when W=128, compare with traditional AHE, our fast adaptive histogram equalization (FAHE) algorithm save about 98.1% and 97.7% processing time for 8bits and 10bits image respectively
- A fast implementation of AHE is given in this paper
- Three pure software techniques are adopted to improve the speed of AHE
- The authors have tested the FAHE on one 476*594 8bits medical image and one 364*1180 10bits industrial Xray image.
- Fig. 2 shows the medical image and processed result (By theoretical analysis the authors know the results are just the same as original AHE).
- As the authors can see from the table, when W=128, compare with traditional AHE, the FAHE algorithm save about 98.1% and 97.7% processing time for 8bits and 10bits image respectively.
- Even compare with IAHE, the algorithm save 12.2% and 21.6% time respectively
- A fast implementation of AHE is given in this paper.
- Three pure software techniques are adopted to improve the speed of AHE.
- Theoretical analysis and experimental results show that it is quite effective.
- It can be realized in nearly real-time on general PC, which is important for medical image processing.
- The techniques the authors used in FAHE could be used in other pixel based histogram equalization algorithm with fixed block size, such as CLAHE (Contrast Limiting Adaptive Histogram Equalization) , CLHE(Constrained Local Histogram Equalization) , MAHE (Multi-scale Adaptive Histogram Equalization) , and some new various local histogram equalization algorithms, etc
- Table1: Pixel computation complexity comparison
- Table2: Average time (ms) for 8bits medical image
- Table3: Average time (ms) for 10bits industrial X-ray image
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