Haze, generated by floaters (semitransparent clouds, fog, snow, etc.) in the atmosphere, can significantly degrade utilization of remote sensing images (RSIs). However, existing techniques for single image dehazing rarely consider that haze is superimposed and shadow, they often aggravate degree shadow dark region. In this paper, a RSI method based on robust light-dark prior (RLDP) proposed, wh...

The clarity of images degrades significantly due to the impact weather conditions such as fog and haze. Persistent particles scatter light, attenuating reflected light from scene, dispersed atmospheric will mix with received by camera affecting image contrast in both outdoor indoor images. Conventionally, scattering model (ATSM) is a often used recover hazy In ATSM, two unknown factors/paramete...

Research on image dehazing has made the need for a suitable dehazed quality assessment (DIQA) method even more urgent. The performance of existing DIQA methods heavily relies handcrafted haze-related features. Since hazy images with uneven haze density distributions will result in after dehazing, manually extracted feature expression is neither accurate nor robust. In this paper, we design deep...

Haze removal has been a very challenging problem due to its ill-posedness, which is more ill-posed if the input data is only a single hazy image. In this paper, we present a new approach for removing haze from a single input image. The proposed method combines the model widely used to describe the formation of a haze image with the assumption in Retinex that an image is the product of the illum...

Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhan...