Memory augment is All You Need for image restoration (2309.01377v1)

Abstract: Image restoration is a low-level vision task, most CNN methods are designed as a black box, lacking transparency and internal aesthetics. Although some methods combining traditional optimization algorithms with DNNs have been proposed, they all have some limitations. In this paper, we propose a three-granularity memory layer and contrast learning named MemoryNet, specifically, dividing the samples into positive, negative, and actual three samples for contrastive learning, where the memory layer is able to preserve the deep features of the image and the contrastive learning converges the learned features to balance. Experiments on Derain/Deshadow/Deblur task demonstrate that these methods are effective in improving restoration performance. In addition, this paper's model obtains significant PSNR, SSIM gain on three datasets with different degradation types, which is a strong proof that the recovered images are perceptually realistic. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/MemoryNet

• The paper introduces MemoryNet, demonstrating improved image restoration by integrating a memory augment layer with contrastive learning.
• It employs a three-granularity memory module that preserves deep image features, enabling robust performance across varied degradations.
• Empirical results show significant PSNR and SSIM gains, underscoring MemoryNet’s superior performance over current state-of-the-art methods.

Memory Augment is All You Need for Image Restoration

This paper presents "Memory Augment is All You Need for Image Restoration," introducing MemoryNet, a neural network architecture designed for enhanced image restoration. This approach effectively tackles common image degradation tasks such as shadow removal, rain removal, and image deblurring, addressing the intrinsic complexity associated with these challenges in low-level computer vision.

The proposed architecture, MemoryNet, is characterized by its use of a memory module and contrastive learning mechanism. The memory module integrates a three-granularity memory layer, which is designed to preserve deep image features. This innovative compartment retains prototypical patterns relevant to the image structure, enhancing the model's capacity to generalize across diverse image degradation scenarios. The memory layer works synergistically with a contrastive learning framework that organizes data into positive, negative, and actual sample triplets. This organization helps in balancing feature learning by bringing the embeddings closer for similar instances while pushing dissimilar ones apart, thus improving the feature extraction process and model robustness.

Empirical results based on various image restoration tasks underpin the paper's claims. Extensive experiments demonstrate MemoryNet's effectiveness across three major datasets with different degradation types. The paper reports significant improvements in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure), indicating that the restored images from MemoryNet are perceptually more realistic compared to previous methods. The effectiveness of MemoryNet is shown not just in quantitative metrics but also in qualitative visual comparisons.

In terms of contributions, the paper outlines several key areas:

1. Network Design: MemoryNet is introduced as a novel end-to-end network featuring a memory augment layer. This layer significantly contributes to generating context-rich and spatially accurate restorations.
2. Memory Augment Layer: This layer models a learnable latent variable to remember global prototypes, which assists in learning more representative image structures.
3. Robust Experimentation: The network is extensively tested on typical image restoration tasks, including shadow removal, rain removal, and deblurring. The results are compared with existing state-of-the-art methods, demonstrating MemoryNet's superior performance both in efficacy and computational complexity.

The paper identifies two major issues within current image restoration techniques: the saturation of model performance upon convergence and the challenge of retaining original image features while removing degradations like shadows. MemoryNet addresses these issues by leveraging memory augment modules to extract low-frequency information and reinforcing model capabilities through contrastive learning.

A significant aspect of MemoryNet is its implication for future AI developments, where memory-based frameworks could potentially enhance model transparency and interpretability in other domains. The memory augment strategy proposes a mechanism to train networks that can memorize and leverage past experiences to improve decision-making in image restoration tasks.

While the results are promising, further exploration into the scalability of MemoryNet for high-resolution image processing and real-time applications could present impactful research avenues. Additionally, integrating similar methodologies within other vision tasks, such as object detection under occlusion, could unveil further potentials of combining memory networks with contrastive learning.

Overall, the introduction of MemoryNet marks a substantial contribution to the domain of image restoration, providing valuable insights and methodologies that inform both theoretical advancements and practical implementations in digital image processing.