针对图像在传输过程中易引入噪声、色彩质量下降、中值滤波导致图像细节丢失和均值滤波出现模糊等问题, 提出了一种可以应用于CMOS图像传感器的图像画质增强和滤波算法。该算法对插值后的Bayer图像数据进行一维空间的增强和降噪处理, 首先将图像从RGB空间转换到YUV空间, 在Y通道上用改进的直方图均衡化方法实现图像明暗程度的对比度增强调节, 对U、V通道采用分段式线性调节方法实现饱和度调节; 然后对Y通道进行自适应降噪, 对U、V通道进行加权中值滤波降噪, 以满足后续处理对图像质量的要求; 最后在Y通道上, 采用基于Laplace算子的锐化掩模进行锐化处理, 保证图像的细节清晰可见。实验结果表明: 从图像视觉效果来看, 相比单独使用中值和均值滤波, 所提出的自适应滤波得到的效果更好, 图像细节保存较好、模糊程度低、图像更为清晰, 且色彩质量更高。通过对比峰值信噪比(PSNR), 对混合噪声进行处理时, 该滤波算法的PSNR优于中值和均值滤波, 有效地抑制了噪声。整个算法在一维邻域空间进行, 更容易在有限的硬件上实现较好的图像处理结果, 满足小面积低功耗的要求。

Aiming at the problems of noise introducing and color quality degradation in the process of image transmission as well as the loss of image details resulting from the median filtering and the blurring resulting from the mean filtering, an image enhancement and filtering algorithm for CMOS image sensor is proposed in this paper. In this algorithm, the interpolated Bayer image data is enhanced and de-noised in the 1-D space. First, the image is converted from the RGB space to the YUV space, in the Y channel, the improved histogram equalization method is used to realize the contrast enhancement, and the U and V channels are segmented by linear adjustment method to achieve saturation adjustment. Then, the Y channel is de-noised adaptively, and the weighted median filtering is used to reduce the noise in U and V channels so as to meet the demand of the subsequent processing. Finally, on the Y channel, a sharpening mask based on Laplace operator is used to sharpen the details of the image. The experimental results show that, compared with the median and mean filtering, our method achieves better visual effect, well preserved details, higher quality of color and almost no blurring. By comparing the peak signal to noise ratio (PSNR), the PSNR of the proposed algorithm is better than the median and mean filtering and the noise is suppressed effectively. Moreover, the whole algorithm is addressed in 1-D neighborhood; it’s easier to realize on limited hardware with small area and low consumption.