High perceptual quality and low distortion degree are two important goals in
image restoration tasks such as super-resolution (SR). Most of the existing SR
methods aim to achieve these goals by minimizing the corresponding yet
conflicting losses, such as the $\ell_1$ loss and the adversarial loss.
Unfortunately, the commonly used gradient-based optimizers, such as Adam, are
hard to balance these objectives due to the opposite gradient decent directions
of the contradictory losses. In this paper, we formulate the
perception-distortion trade-off in SR as a multi-objective optimization problem
and develop a new optimizer by integrating the gradient-free evolutionary
algorithm (EA) with gradient-based Adam, where EA and Adam focus on the
divergence and convergence of the optimization directions respectively. As a
result, a population of optimal models with different perception-distortion
preferences is obtained. We then design a fusion network to merge these models
into a single stronger one for an effective perception-distortion trade-off.
Experiments demonstrate that with the same backbone network, the
perception-distortion balanced SR model trained by our method can achieve
better perceptual quality than its competitors while attaining better
reconstruction fidelity. Codes and models can be found at
this https URL

通过集成梯度自由进化算法与基于梯度的 Adam 算法，构建了一个新的优化器，将超分辨率中的感知和失真权衡问题转化为多目标优化问题，并通过融合网络将不同感知 - 失真偏好的最佳模型合并为一个更强大的模型，实验表明，我们的方法训练得到的感知 - 失真平衡的超分辨率模型能够在感知质量和重建保真度方面优于竞争方法。

感知 - 失真平衡超分辨率：多目标优化视角

Perception-Distortion Balanced Super-Resolution: A Multi-Objective  Optimization Perspective

We consider the image transmission problem over a noisy wireless channel via
deep learning-based joint source-channel coding (DeepJSCC) along with a
denoising diffusion probabilistic model (DDPM) at the receiver. Specifically,
we are interested in the perception-distortion trade-off in the practical
finite block length regime, in which separate source and channel coding can be
highly suboptimal. We introduce a novel scheme that utilizes the range-null
space decomposition of the target image. We transmit the range-space of the
image after encoding and employ DDPM to progressively refine its null space
contents. Through extensive experiments, we demonstrate significant
improvements in distortion and perceptual quality of reconstructed images
compared to standard DeepJSCC and the state-of-the-art generative
learning-based method. We will publicly share our source code to facilitate
further research and reproducibility.

通过深度学习的联合源信道编码（DeepJSCC）和接收端的去噪扩散概率模型（DDPM），我们考虑在噪声无线信道上的图像传输问题，并在实际有限块长度范围内关注感知失真权衡，其中独立的源编码和信道编码可能高度亚最优。我们引入了一种利用目标图像的范围 - 零空间分解的新方案。我们经过编码传输图像的范围空间，并采用 DDPM 逐步改进其零空间内容。通过大量实验，我们证明与标准 DeepJSCC 和最先进的生成学习方法相比，重建图像的失真和感知质量有显着改进。我们将公开共享我们的源代码以促进进一步的研究和可重复性。