基于自适应块组割先验的噪声图像超分辨率重建

要增强噪声图像的分辨率，传统的串联方式依次进行去噪与超分辨率重建两个步骤，但去噪算法去除噪声的同时也损失了部分细节信息，影响了后续超分辨率重建的质量.为了使低分辨率噪声图像中所有细节信息都能参与超分辨率重建，本文以非局部中心化稀疏表示（Nonlocally centralized sparse representation，NCSR）模型为基础，提出了基于自适应块组割（Patch-group-cuts，PGCuts）先验的噪声图像超分辨率重建方法，同时实现去噪和超分辨率重建功能.块组割先验基于新颖的三维邻域系统和块组模型，能够达到图像去噪、边缘平滑和边缘清晰等效果.重建时以边缘强度为参考对块组割先验进行自适应约束，由于块组割在平滑区域约束力较低，采用分区域融合的方式进一步抑制噪声.本文对合成的低分辨率噪声图像和真实的低分辨率噪声图像进行了重建实验，实验表明，基于自适应块组割先验的噪声图像超分辨率重建算法，在丰富细节的同时能抑制噪声的干扰，不但具有较高的峰值信噪比和结构相似度等客观评价值，而且在非光滑区域具有很好的主观重建效果.

Noisy Image Super-resolution Reconstruction with Adaptive Patch-group-cuts Prior

To enhance resolution of a noisy image, the conventional method adopts a cascaded scheme of denoising followed by super-resolution (SR) reconstruction. However, the denoising algorithm inevitably causes some loss of high-frequency information in the image, especially in non-smooth regions, which significantly influences the quality of the subsequent SR reconstruction. To incorporate all the high-frequency information from the noisy low-resolution (LR) images into the SR reconstruction, a noisy image SR method with adaptive patch-group-cuts (PGCuts) prior is proposed, based on the nonlocally centralized sparse representation (NCSR) model. The proposed method performs denoising and SR reconstruction simultaneously. The PGCuts prior, which is built on a novel 3D neighborhood system and a patch-group model, is able to denoise the image, restore smooth and sharp edges, etc. The edge strength measurement is introduced to adaptively balance the constraint strength of PGCuts prior in reconstruction. As PGCuts constraint is weak in smooth regions, a region-based fusion scheme is also used to further suppress the noise. Reconstruction experiments are conducted on both synthesized and real noisy LR images. It is demonstrated that the proposed method can restore plenty of details in reconstructed SR images while still suppress the noise, giving not only high scores in objective criteria like PSNR and SSIM, but also very good visual effects in non-smooth regions in subjective evaluations.