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Contrast Enhancement And Brightness Preservation Using Multi- Decomposition Histogram Equalization

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arxiv 1307.3054 v1 pith:I5RF3JA3 submitted 2013-07-11 cs.CV

classification cs.CV
keywords equalizationhistogramimagebrightnesscontrastenhancementmdhemethods
verification ladder T0 review T1 audit T2 compute T3 formal
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Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization (CHE), Adaptive Histogram Equalization (ADHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, however, brightness is not well preserved with these methods, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input sixty-four parts, applied CHE in each of the sub-images and then finally interpolated them in correct order. The final image after MDHE results in contrast enhanced and brightness preserved image compared to all other techniques mentioned above. We have calculated the various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported by bar graphs, histograms and the parameter calculations at the end.

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  1. TRUDI and TITUS: A Multi-Perspective Dataset and A Three-Stage Recognition System for Transportation Unit Identification

    cs.CV 2025-08 conditional novelty 5.0 of 10

    TRUDI is a new public benchmark of 35,034 labeled port images from aerial and ground views, with a three-stage TITUS pipeline that reads IDs but achieves only 12-22 percent end-to-end accuracy.

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