Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2002.01155v1)

Abstract: In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement. We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference. Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples. By thorough experimental evaluation on the UFO-120 and other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution. We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects. Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots. The model and dataset information will be available at: https://github.com/xahidbuffon/Deep-SESR.

• The paper presents Deep SESR which concurrently enhances underwater images and boosts resolution up to fourfold using a residual-in-residual network.
• The method integrates dense residual blocks, a feature extraction network, and an auxiliary attention module to facilitate multi-scale feature learning and precise saliency prediction.
• Experimental evaluations on UFO-120 and standard datasets show significant improvements in PSNR, SSIM, and UIQM, validating its real-time applicability in both underwater and terrestrial scenarios.

Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

The paper "Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception" presents a novel approach to addressing issues related to underwater image degradation, proposing a unified solution that incorporates simultaneous enhancement and super-resolution (SESR) via a deep learning framework suited for real-time applications. The core contribution, Deep SESR, employs a residual-in-residual network structure, tactically engineered to manage perceptually enhanced image generation and saliency prediction, achieving up to a fourfold increase in spatial resolution.

Methodological Overview

Deep SESR's architecture is distinctive due to its integration of dense residual blocks (RDBs), a feature extraction network (FENet), and an auxiliary attention network (AAN). This setup facilitates multi-scale hierarchical feature learning through which both enhancement and super-resolution are achieved concurrently. The proposed method applies a sophisticated multi-modal objective function to guide its learning process, addressing chrominance-specific color degradation, color contrast, and sharpness. By exploiting a shared feature space, the model efficiently predicts the saliency of foreground regions while simultaneously enhancing global contrast.

An integral aspect of the paper is the introduction of UFO-120, a substantial dataset intentionally curated to support large-scale SESR training. This dataset comprises over 1,500 samples for training and 120 for benchmarking, augmented with comprehensive annotations necessary for SESR learning.

Experimental Evaluation and Results

The paper showcases the superiority of Deep SESR through extensive experimental evaluations using the UFO-120 and other standard datasets. It provides quantitative performance metrics, including PSNR, SSIM, and UIQM, demonstrating that Deep SESR significantly outpaces existing state-of-the-art solutions for underwater image enhancement and super-resolution. Furthermore, the model's robustness is affirmed through testing with images exhibiting varying spectral and spatial degradation, as well as terrestrial images encompassing unseen object types. The findings also reveal Deep SESR's potential for broader applications beyond underwater scenarios, underlining its efficacy with terrestrial data without additional modifications.

Implications and Future Directions

Deep SESR contributes a significant advancement in the field of underwater robotic vision by enabling visually-guided robots to operate more effectively in real-time scenarios. Its ability to intelligently enhance image quality without the heavy computational load typical of separate enhancing and super-resolving steps is particularly valuable for deployment on resource-constrained platforms, such as single-board computers.

For practical implications, Deep SESR holds promising applications not only in underwater exploration and monitoring but also in enhancing terrestrial visual perception where conditions are less predictable. The insights provided by the saliency map further augment the framework's applicability, enhancing attention modeling for both robotic and non-robotic systems.

In conclusion, the Deep SESR framework is an innovative stride towards unifying image processing tasks, signaling a step forward in efficient and effective underwater image analysis. Future work could explore expanding its capabilities to support higher scale resolution tasks and further optimize its performance in diverse environmental conditions, potentially leveraging emerging technology trends in computational resources and algorithms.