Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code and models are available at https://github.com/seungho-snu/SROOE.
翻译:受过视觉和对抗性损失训练的单一图像超分辨率(SISR)网络与受过扭曲性损失训练的网络(如L1或L2)相比具有高调产出,因此,我们提议一个新的SISR框架,将每个区域的最佳目标用于在高分辨率产出的整体领域产生可信的结果,但具体地说,这一框架包括两种模型:一种预测模型,用来推断给定的低分辨率(LR)投入和异常细节的最佳目标地图,以及一种使用目标目标目标地图来产生相应的SR产出的基因化模型;一种基因化模型是针对我们提议的反映一套基本目标的客观轨迹进行的培训,使单一网络能够从轨迹上了解与视觉-视觉损失相匹配的各种结果;一种预测性模型,用来推断一种低分辨率(LR)投入或非自然细节;一种配置模型,用来推导出一个目标目标图图;一种预测性模型,用来显示SISRMR-S-S-S-S-S-S-S-S-S-S-S-S-SAR-SAR-SAR-S-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SARalalalalalal-SAR 和SAR-SAR-SAR-S-R-R-SAR-SAR-SAR-SAR-SAR-S-S-S-S-S-S-SAR-SAR-SAR-SAR-SAR-SAR-SAR-Sy-SAR-SAR-SAR-SAR-S-SAR-SAR-S-SAR-S-S-S-S-S-SAR-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-