Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining (1808.01491v1)

Abstract: Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design. In this paper, we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning. Specifically, we propose a non-locally enhanced encoder-decoder network framework, which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail. The proposed encoder-decoder framework is composed of a series of non-locally enhanced dense blocks that are designed to not only fully exploit hierarchical features from all the convolutional layers but also well capture the long-distance dependencies and structural information. Extensive experiments on synthetic and real datasets demonstrate that the proposed method can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods.

• The paper introduces a non-locally enhanced encoder-decoder network that leverages non-local operations and dense blocks to capture long-range dependencies for de-raining tasks.
• The innovative dense block design enables adaptive rain streak modeling and detail preservation, leading to superior PSNR and SSIM results.
• Extensive experiments on synthetic and real datasets validate the framework’s effectiveness, setting a new benchmark for single image de-raining.

Overview of Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

The paper explores an advanced method for addressing the challenge of rain streaks removal from single images, a pertinent task within computer vision and multimedia applications. Previous methodologies either rely on decomposing images into different features or employ multi-stage network approaches, highlighting a shortfall in leveraging the full potential of neural network designs. This research introduces a novel framework named Non-locally Enhanced Encoder-Decoder Network (NLEDN), which significantly improves rain streaks modeling while preserving image details.

Key Contributions

1. Framework Design: The proposed NLEDN framework utilizes a non-locally enhanced encoder-decoder network architecture. This is designed to capture complex spatial dependencies and enhance feature expression effectively. It consists of a series of non-locally enhanced dense blocks (NEDBs) which are critical in successfully modeling long-distance dependencies and leveraging hierarchical feature extraction.
2. Dense Block Architecture: The dense block architecture within NEDNs includes a non-local operation that computes feature responses over a range of spatial positions instead of relying solely on local surrounding regions. This wider context enables the network to address the inherent difficulty of removing long rain streaks, which traditional deep learning models struggle with due to their limited local receptive fields.
3. Empirical Validation: Extensive experiments on both synthetic and real datasets demonstrate significant improvements in rain-streaks removal. Specifically, NLEDNs consistently outperform several state-of-the-art methods, as evidenced by stronger numerical results in metrics like PSNR and SSIM across multiple benchmark datasets.

Theoretical and Practical Implications

The introduction of non-local operations within the encoder-decoder architecture represents a critical shift in neural network design for image restoration tasks. This paper's insights suggest potential improvements in other areas, such as image denoising and super-resolution, where long-distance spatial dependencies play a crucial role.

Practically, the NLEDN framework offers enhanced capabilities for multimedia applications, potentially impacting fields ranging from driverless technology to advanced image editing. This improvement also contributes to better performance in related computer vision systems affected by environmental conditions like rain.

Future Directions

The promising results and robust framework offered by the NLEDN suggest valuable avenues for further research. One is the potential application of non-local operations beyond image de-raining, to tasks demanding intricate spatial feature extraction. Additionally, optimizing network efficiency to accommodate real-time processing demands in practical applications could be a worthwhile pursuit, expanding the framework's utility.

This paper substantially advances the methodology for single-image rain streak removal, presenting a structurally innovative approach that can inspire subsequent research in image restoration and enhancement domains.