This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients...This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.展开更多
针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度...针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度分离的特性,对V分量使用多尺度自引导锐化-平滑图像滤波,准确估计光照分量进而求得精确的反射分量.其次,针对光照分量分布不均的问题,提出一种二维自适应伽马变换算法并通过大量对比选取最佳参数,对较暗区域亮度进行拉伸,同时抑制较亮区域的亮度,使整体图像光照更加均匀,图像亮度更符合人眼视觉.再次,针对反射分量存在部分边缘模糊与噪声的问题,提出多尺度钝化掩蔽算法,在抑制噪声的同时能够有效增强图像细节信息,提升整体图像动态范围.最后,对S分量使用自适应饱和度增强算法,将增强后的S分量、V分量与保持不变的H分量合并转到RGB图像,并与带色彩恢复的多尺度视网膜增强算法(Multi-Scale Retinex with Color Restoration,MSRCR)中的色彩恢复因子结合得到最终增强图像.实验结果表明:所提低照度图像增强算法的基于精细自然场景统计的图像质量盲评价指标和平均梯度较其他对比算法分别提高了14.62%、32.10%,不仅能够有效地解决图像亮度分布不均问题,而且能够提高图像轮廓细节的丰富程度和对比度,整体效果优于其他对比算法.展开更多
基金supported by the National Natural Science Foundation of China (61101208)
文摘This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.
文摘针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度分离的特性,对V分量使用多尺度自引导锐化-平滑图像滤波,准确估计光照分量进而求得精确的反射分量.其次,针对光照分量分布不均的问题,提出一种二维自适应伽马变换算法并通过大量对比选取最佳参数,对较暗区域亮度进行拉伸,同时抑制较亮区域的亮度,使整体图像光照更加均匀,图像亮度更符合人眼视觉.再次,针对反射分量存在部分边缘模糊与噪声的问题,提出多尺度钝化掩蔽算法,在抑制噪声的同时能够有效增强图像细节信息,提升整体图像动态范围.最后,对S分量使用自适应饱和度增强算法,将增强后的S分量、V分量与保持不变的H分量合并转到RGB图像,并与带色彩恢复的多尺度视网膜增强算法(Multi-Scale Retinex with Color Restoration,MSRCR)中的色彩恢复因子结合得到最终增强图像.实验结果表明:所提低照度图像增强算法的基于精细自然场景统计的图像质量盲评价指标和平均梯度较其他对比算法分别提高了14.62%、32.10%,不仅能够有效地解决图像亮度分布不均问题,而且能够提高图像轮廓细节的丰富程度和对比度,整体效果优于其他对比算法.
