Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization

核心概念

Entropy-based quantification and contrastive optimization address over-smoothing in image super-resolution models.

摘要

The article discusses the challenge of over-smoothing in PSNR-oriented image super-resolution models. It introduces the Center-oriented Optimization (COO) problem, where models converge towards the center point of similar high-resolution images rather than the ground truth. The impact of data uncertainty on this problem is quantified using entropy. A novel solution called Detail Enhanced Contrastive Loss (DECLoss) is proposed to reduce variance in potential high-resolution distribution, improving perceptual quality. Experimental results show enhancements in PSNR-oriented and GAN-based models, achieving state-of-the-art performance.

統計資料

PSNR: 24.51
LPIPS: 0.093
Downsampled Urban100: 4x

引述

"Implicitly optimizing the COO problem, perceptual-driven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed."
"We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss)."
"With the assistance of DECLoss, these methods can surpass a variety of state-of-the-art methods on perceptual metric LPIPS while preserving a high PSNR score."

從以下內容提煉的關鍵洞見

Uncovering the Over-smoothing Challenge in Image Super-Resolution

by Tianshuo Xu,... 於 arxiv.org 03-18-2024

https://arxiv.org/pdf/2201.01034.pdf

深入探究

How does increasing model capacity affect entropy management?

Increasing the model capacity can have a significant impact on entropy management in image super-resolution. As the model's capacity increases, its ability to learn and distinguish between similar low-resolution inputs improves. This enhanced discriminative power leads to a reduction in the potential range of values for the high-resolution conditional distribution given a specific low-resolution input, ultimately decreasing the entropy of the data distribution. By reducing entropy through increased model capacity, the output generated by the model can be closer to the true high-resolution distribution.

What are the drawbacks of transforming SR into a generative framework?

Transforming image super-resolution (SR) into a generative framework comes with certain drawbacks. One major drawback is that generative frameworks often introduce a high degree of randomness in their results, leading to poor consistency with input images. Additionally, these methods may struggle with maintaining fidelity to ground truth details and textures while generating realistic images. The complexity and computational resources required for training such models can also pose challenges in terms of efficiency and scalability. Furthermore, there may be difficulties in controlling or fine-tuning specific aspects of image generation when using generative frameworks for SR tasks.

How does contrastive learning improve perceptual quality in image super-resolution?

Contrastive learning plays a crucial role in improving perceptual quality in image super-resolution by addressing issues such as over-smoothing and enhancing detail preservation. By utilizing contrastive learning techniques at patch-level granularity, it becomes possible to reduce variance within potential high-resolution distributions effectively.
Specifically:

Selection of Positive and Negative Samples: Contrastive learning enables selecting positive samples (similar patches) based on PSNR similarity metrics above a threshold η and negative samples (dissimilar patches) below η.
Cosine Similarity Constraints: Cosine similarities are computed between predicted high-res patches ˆPy & ground truth HR patches Py (Ssr→hr), as well as between predicted high-res patches ˆPy & other predicted HR patches ˆPy (Ssr→sr).
Entropy Reduction: By polarizing details through contrastive loss optimization, variance within HR distributions is reduced, leading to decreased information entropy.
Improved Consistency: Contrastive learning helps generate more visually pleasing results by minimizing distances between positive samples while maximizing distances between negative samples.
Overall, contrastive learning aids in producing higher-quality SR images with enhanced perceptual features while mitigating issues related to over-smoothing commonly seen in traditional PSNR-oriented models.