This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.

Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. Our method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. We are the first to demonstrate the utility and effectiveness of a fusion-based technique for dehazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

This article introduces a novel solution to improve image enhancement in terms of color appearance. Our approach, called Color Channel Compensation (3C), overcomes artifacts resulting from the severely non-uniform color spectrum distribution encountered in images captured under hazy night-time conditions, underwater, or under non-uniform artificial illumination. Our solution is founded on the observation that, under such adverse conditions, the information contained in at least one color channel is close to completely lost, making the traditional enhancing techniques subject to noise and color shifting. In those cases, our pre-processing method proposes to reconstruct the lost channel based on the opponent color channel. Our algorithm subtracts a local mean from each opponent color pixel. Thereby, it partly recovers the lost color from the two colors (red-green or blue-yellow) involved in the opponent color channel. The proposed approach, whilst simple, is shown to consistently improve the outcome of conventional restoration methods. To prove the utility of our 3C operator, we provide an extensive qualitative and quantitative evaluation for white balancing, image dehazing, and underwater enhancement applications.

This papers introduces a new single image dehazing approach. The method employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by specific maps in order to yield accurate haze-free results. The method computes in a per-pixel fashion being straightforward to be implemented. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.

This paper introduces an effective decolorization algorithm that preserves the appearance of the original color image. Guided by the original saliency, the method blends the luminance and the chrominance information in order to conserve the initial color disparity while enhancing the chromatic contrast. As a result, our straightforward fusing strategy generates a new spatial distribution that discriminates better the illuminated areas and color features. Since we do not employ quantization or a per-pixel optimization (computationally expensive), the algorithm has a linear runtime, and depending on the image resolution it could be used in real-time applications. Extensive experiments and a comprehensive evaluation against existing state-of-the-art methods demonstrate the potential of our grayscale operator. Furthermore, since the method accurately preserves the finest details while enhancing the chromatic contrast, the utility and versatility of our operator have been proved for several other challenging applications such as video decolorization, detail enhancement, single image dehazing and segmentation under different illuminants.