End-to-end Alternating Optimization for Blind Super Resolution (2105.06878v1)

Abstract: Previous methods decompose the blind super-resolution (SR) problem into two sequential steps: \textit{i}) estimating the blur kernel from given low-resolution (LR) image and \textit{ii}) restoring the SR image based on the estimated kernel. This two-step solution involves two independently trained models, which may not be well compatible with each other. A small estimation error of the first step could cause a severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from the LR image, which makes it difficult to predict a highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate the blur kernel and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR image based on the predicted kernel, and \textit{Estimator} estimates the blur kernel with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, \textit{Estimator} utilizes information from both LR and SR images, which makes the estimation of the blur kernel easier. More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}. Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at a much higher speed. The source code is available at \url{https://github.com/greatlog/DAN.git}.

• The paper introduces an integrated approach that alternates between convolutional kernel estimation and super resolution image restoration in a unified framework.
• It employs dual-path conditional blocks to maintain strong correlations between inputs and outputs, effectively preventing premature convergence to fixed points.
• The method outperforms state-of-the-art models, demonstrating improved PSNR and SSIM metrics across both synthetic and real-world image datasets.

End-to-End Alternating Optimization for Blind Super Resolution: An Analysis

Introduction

The paper presents a sophisticated approach for tackling the challenging problem of blind super resolution (SR) through an end-to-end alternating optimization framework. Traditionally, blind SR decouples into two distinct and sequential processes: blur kernel estimation and the subsequent use of this kernel to restore a high-resolution (HR) image from a low-resolution (LR) counterpart. Although intuitive, this process can entail significant drawbacks due to model incompatibilities and compounded errors from kernel estimation to SR.

Methodology

Central to this paper is the implementation of an end-to-end deep alternating network (DAN) aimed at integrating blur kernel estimation and SR image restoration into a cohesive model. This approach hinges on alternating optimization which eschews the isolated estimates-validation cycle typical of earlier methods. The process repeatedly iterates between two convolutional neural modules: the Restorer for obtaining the SR image with a given kernel and the Estimator for refining the kernel using the restored image.

The Estimator and Restorer are intricately designed convolutional sub-networks. A significant innovation here is the dual-path conditional block (DPCB) within these modules, which allows simultaneous processing of the primary and conditionally dependent inputs. This dual-path architecture aims to maintain strong correlation between the inputs and outputs, preventing the optimization loop from collapsing into a fixed point too early during the iteration.

Results

The proposed model was extensively tested on both synthetic datasets with controlled blur parameters and real-world images. Notably, the model surpassed state-of-the-art approaches, demonstrating superior performance in both speed and the quality of super-resolved images. The paper highlights the robustness of the parameter estimation in adverse conditions, showing resiliency to variability introduced by different blur kernels.

Quantitatively, DAN's results were benchmarked against notable models such as IKC and KernelGAN+ZSSR across standardized datasets (e.g., Set5, Set14). The model showed consistent improvements in PSNR and SSIM metrics, exemplifying its broad applicability to diverse degradation settings. Moreover, kernel visualization tests revealed that the DAN maintains greater fidelity to ground-truth representations compared to competing methods.

Implications and Future Directions

This paper has broad implications for both theoretical and practical advancements in image enhancement and related fields. The compelling results demonstrated by the end-to-end alternating optimization framework signify a noteworthy stride towards more integrated and adaptive super resolution methodologies. Not only could it find direct applicability in fields like video enhancement and medical imaging, but the core ideas could also be translated to other inverse problems in computer vision and beyond.

Looking ahead, it will be intriguing to examine how such frameworks might adapt to more complex degradation models that include non-Gaussian noise or real-world intricacies like motion blur. Furthermore, extending this work to larger scale, unlabeled real-world datasets can refine these methodologies to be even more generalizable.

In conclusion, this paper introduces a cohesive and computationally efficient solution for blind SR, substantiated by robust empirical evidence. The unleashing of both theoretical and practical enhancements through end-to-end models is an exciting development, one that could curb the gap between synthesized and real-world applications in low-level vision.