Sandstorm Image Enhancement Using Image-Adaptive Eigenvalue and Brightness-Adaptive Dark Channel Network

Abstract

Sandstorm images suffer from dust particles and the attenuation of light. Degraded sandstorm images have a reddish or yellowish color cast owing to dust particles, which have a certain color. Because of this phenomenon, distorted sandstorm images have imbalanced red and blue channels. Existing sandstorm enhancement methods improve the image by only focusing on dehazing, due to sandstorm images having similar features to hazy or dusty images. However, the enhanced images using previous methods have a color cast. Therefore, to enhance the sandstorm image naturally, a balancing procedure of color components is needed. This paper proposes a color-balancing method using image-adaptive eigenvalues. Because eigenvalues describe image characteristics, improved images have balanced color components. However, these enhanced images have dusty features. Therefore, to enhance balanced images a dehazing procedure is needed, something which this paper applies using a multiscale convolution neural network, thus generating a transmission map with brightness-adaptive features. Images enhanced using the proposed method compare well with state-of-the-art methods in subjective and objective measures of quality.

Keywords

• sandstorm image enhancement
• image signal processing
• image-adaptive eigenvalue
• brightness-adaptive dark channel network
• convolution neural network