ﻻ يوجد ملخص باللغة العربية
For underwater applications, the effects of light absorption and scattering result in image degradation. Moreover, the complex and changeable imaging environment makes it difficult to provide a universal enhancement solution to cope with the diversity of water types. In this letter, we present a novel underwater image enhancement (UIE) framework termed SCNet to address the above issues. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Considering the diversity of degradation is mainly rooted in the strong correlation among pixels, we apply whitening to de-correlates activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such latent encodings, the decoder can easily reconstruct the clean signal, and unaffected by the distortion types caused by the water. Experimental results on two real-world UIE datasets show that the proposed approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement
Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and achieve outstanding performance. However, these methods ignore the significant domain gap bet
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided
Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object
Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms a