Dictionary learning can be used for image superresolution by learning a pair
of coupled dictionaries of image patches from high-resolution and
low-resolution image pairs such that the corresponding pairs share the same
sparse vector when represented by the coupled dictionaries. These dictionaries
then can be used to to reconstruct the corresponding high-resolution patches
from low-resolution input images based on sparse recovery. The idea is to
recover the shared sparse vector using the low-resolution dictionary and then
multiply it by the high-resolution dictionary to recover the corresponding
high-resolution image patch. In this work, we study the effect of the sparse
recovery algorithm that we use on the quality of the reconstructed images. We
offer empirical experiments to search for the best sparse recovery algorithm
that can be used for this purpose.

通过学习高分辨率和低分辨率图像对的一对耦合字典，字典学习可以用于图像超分辨率，以使表示为相应字典的稀疏向量相同。然后可以使用这些字典基于稀疏恢复从低分辨率输入图像重新构建相应的高分辨率补丁。本研究探讨了所使用的稀疏恢复算法对重建图像质量的影响，并通过实证实验寻找可用于此目的的最佳稀疏恢复算法。

稀疏恢复方法对图像超分辨率质量的影响研究

Exploring the Effect of Sparse Recovery on the Quality of Image  Superresolution

With advancement in deep neural network (DNN), recent state-of-the-art (SOTA)
image superresolution (SR) methods have achieved impressive performance using
deep residual network with dense skip connections. While these models perform
well on benchmark dataset where low-resolution (LR) images are constructed from
high-resolution (HR) references with known blur kernel, real image SR is more
challenging when both images in the LR-HR pair are collected from real cameras.
Based on existing dense residual networks, a Gaussian process based neural
architecture search (GP-NAS) scheme is utilized to find candidate network
architectures using a large search space by varying the number of dense
residual blocks, the block size and the number of features. A suite of
heterogeneous models with diverse network structure and hyperparameter are
selected for model-ensemble to achieve outstanding performance in real image
SR. The proposed method won the first place in all three tracks of the AIM 2020
Real Image Super-Resolution Challenge.

该论文基于高斯过程的神经架构搜索方案，通过多种不同的网络结构和超参数选择进行模型集成，设计出一个用于实际图像高清化的深度残差网络，在 AIM 2020 实际图像高清化挑战赛上荣获三项第一。

通过异构模型集成和 GP-NAS 实现真实图像超分辨率

Real Image Super Resolution Via Heterogeneous Model Ensemble using  GP-NAS

Recovering images from undersampled linear measurements typically leads to an
ill-posed linear inverse problem, that asks for proper statistical priors.
Building effective priors is however challenged by the low train and test
overhead dictated by real-time tasks; and the need for retrieving visually
"plausible" and physically "feasible" images with minimal hallucination. To
cope with these challenges, we design a cascaded network architecture that
unrolls the proximal gradient iterations by permeating benefits from generative
residual networks (ResNet) to modeling the proximal operator. A mixture of
pixel-wise and perceptual costs is then deployed to train proximals. The
overall architecture resembles back-and-forth projection onto the intersection
of feasible and plausible images. Extensive computational experiments are
examined for a global task of reconstructing MR images of pediatric patients,
and a more local task of superresolving CelebA faces, that are insightful to
design efficient architectures. Our observations indicate that for MRI
reconstruction, a recurrent ResNet with a single residual block effectively
learns the proximal. This simple architecture appears to significantly
outperform the alternative deep ResNet architecture by 2dB SNR, and the
conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For
image superresolution, our preliminary results indicate that modeling the
denoising proximal demands deep ResNets.

本文提出了一个级联的神经网络架构来解决基于低采样率的线性逆问题。其中将深度生成式残差网络和像素级和感知损失函数相结合，成功地提高了 MRI 重建的信噪比而将推理速度增加了一百倍，并且也证明了通过设计的 ResNet 可以更好地建模去噪近端来提高图像的超分辨率。