基于稀疏编码的红外显著区域超分重建算法

由于红外光学衍射限和红外探测器的局限,得到的红外图像噪声相对偏大,分辨率偏低。对红外图像进行超分辨率重建可以提高图像分辨率,但同时又会增强背景噪声。针对此问题,提出了基于稀疏编码的红外显著区域超分重建算法,将超分重建和显著度检测相结合,可以提高目标分辨率并降低背景噪声。首先采用双层卷积提取图像特征,并自适应选择图像信息熵较大的图像块用于训练联合字典。然后利用稀疏特征计算显著度获取显著区域,再将显著区域用训练好的字典进行超分辨重建,与目标无关的背景区域采用高斯滤波。实验结果显示改进的重建算法在同等条件下重建效果优于重建模型ScSR和SRCNN,图像信噪比提高3～4倍。

Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding

Due to the limitations of infrared optical diffraction and infrared detectors, the noise of infrared images is relatively large and the resolution is low. Super-resolution reconstruction of infrared images improves image resolution, but at the same time enhances the noise of background. Aiming at this problem, a salience region super-resolution reconstruction algorithm for infrared images based on sparse coding is proposed. Combining the saliency detection and the super-segment reconstruction improves the target definition and reduces the background noise. Firstly, image feature is extracted by double-layer convolution, and image patches with large entropy are adaptively selected for training the joint dictionary. Sparse features are used to calculate the saliency to obtain salient regions, which reconstructs image patches in saliency region by the trained dictionary, and the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is better than ScSR and SRCNN under the same conditions, and the image signal-to-noise ratio is increased by 3-4 times.