Bit-depth expansion for noisy contour reduction in natural images
Bit-depth expansion for noisy contour reduction in natural images
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DOI：
10.1109/icassp.2016.7471961
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Akira Mizuno;M. Ikebe
中科院分区：
文献类型：
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作者：
Akira Mizuno;M. Ikebe
我们提出了一种针对自然图像的位深度扩展（BDE）方法。在成像系统的模拟部分中，由于噪声（例如，图像传感器中的热噪声）而发生信号强度波动。之后，在数字部分中，强度被舍入到有限的水平。后一个过程，即量子化，增加了由随机共振引起的波动误差的强度。这些误差被视为灰度区域中的假轮廓伪影。我们的目标是从量化的噪声信号中获得原始信号。我们制定了一个概率模型的基础上，这个量化过程中，并成功地重建光滑的灰度从嘈杂的轮廓。通过投票的主观评价表明，输出图像具有更高的质量。
We propose a bit-depth expansion (BDE) method targeting natural images. In the analog part of an imaging system, signal intensity fluctuations occur due to noise (e.g. thermal noise in the image sensor). After that, in the digital part, the intensities are rounded off to limited levels. The latter process, which is quantization, increases the intensity of fluctuation errors caused by stochastic resonance. These errors are viewed as false contour artifacts in the gradation region. Our goal was to obtain the original signal from the quantized noisy signal. We formulated a probabilistic model based on this quantization process, and successfully reconstructed smooth gradations from noisy contours. Subjective evaluation by voting clarified that the output image has higher quality.