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Optimized contrast enhancement for real- time image and video dehazing. Abstract. A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real- time video dehazing.
A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in. Increase the Contrast of a PDF to Sharpen & Darken Text. With Preview you can adjust the contrast of a PDF, this makes the text sharper and darker, and.
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We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real- time dehazing applications.
Highlightsв–є We propose a fast and optimized dehazing algorithm for hazy images and videos. We restore a hazy image by enhancing the contrast. The proposed algorithm enhances the contrast and preserves the information optimally.
We reduce flickering artifacts in a dehazed video. The proposed algorithm is sufficiently fast for real- time applications. Keywords. Image dehazing; Video dehazing; Image restoration; Contrast enhancement; Temporal coherence; Image enhancement; Optimized dehazing; Atmospheric light estimation. Introduction. An image, captured in bad weather, often yields low contrast due to the presence of haze in the atmosphere, which attenuates scene radiance.
Low contrast images degrade the performance of various image processing and computer vision algorithms. Dehazing is the process of removing haze from hazy images and enhancing the image contrast. Histogram equalization or unsharp masking can be employed to enhance the image contrast by stretching the histogram [1]. However, these methods do not consider that the haze thickness is proportional to object depths, which are locally different in an image. Thus, they cannot compensate the contrast degradation in a hazy image adaptively. More sophisticated dehazing algorithms first estimate object depths in a scene.
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Several dehazing algorithms have been proposed to estimate object depths using multiple images or additional information. For example, object depths are estimated from two images, which are captured in different weather conditions [2] and [3] or with different degrees of polarization [4] and [5]. Also, Kopf et al. These algorithms can estimate scene depths and remove haze effectively, but require multiple images or additional information, which limits their applications. Recently, single image dehazing algorithms have been developed to overcome the limitation of multiple image dehazing approaches. These algorithms make use of strong assumptions or constraints to remove haze from a single image.
Tan [7] maximized the contrast of a hazy image, assuming that a haze- free image has a higher contrast ratio than the hazy image. Tan’s algorithm, however, tends to overcompensate for the reduced contrast, yielding halo artifacts. Fattal [8] decomposed the scene radiance of an image into the albedo and the shading, and then estimated the scene radiance based on independent component analysis (ICA), assuming that the shading and the object depth are locally uncorrelated. It can remove haze locally but cannot restore densely hazy images. Kratz and Nishino [9] estimated the albedo and the object depth jointly by modeling a hazy image as a factorial Markov random field (FMRF).
Tarel and Hautiere [1. He et al. [1. 1] estimated object depths in a hazy image based on the dark channel prior, which assumes that at least one color channel should have a small pixel value in a haze- free image. They also applied an alpha matting scheme to refine the object depths. Ancuti et al. [1. He et al.’s algorithm by modifying the block- based approach to a layer- based one. In addition, He et al.’s algorithm has been adopted and improved in many algorithms [1. For video dehazing, Tarel et al.
They partitioned a hazy video sequence into dynamically varying objects and a planar road, and then updated the scene depths only for the objects using the still image dehazing scheme in [1. Also, Zhang et al. Oakley and Bu [1. Their algorithm is computationally simple, but it cannot adaptively remove haze when a captured image has variable scene depths.
The existing dehazing algorithms often exhibit overstretched contrast [7], [9], [1. To overcome these drawbacks, the contrast enhancement should be controlled more adaptively. Furthermore, the conventional video dehazing algorithms suffer from huge computational complexity [1. Therefore, an efficient real- time video dehazing algorithm is required for a wide range of practical applications. In this work, we propose a fast dehazing algorithm for images and videos based on the optimized contrast enhancement. The proposed algorithm is based on our preliminary work on static image dehazing [2. We increase the contrast of a restored image to remove haze.
However, if the contrast is overstretched, some pixel values are truncated by overflow or underflow. We design a cost function to alleviate this information loss while maximizing the contrast. Then, we find the optimal scene depth for each block by minimizing the cost function.
Furthermore, for video dehazing, assuming that the scene radiance of an object point is invariant between adjacent frames, we add a temporal coherence cost to the total cost function. We also implement a parallel computing scheme for fast dehazing. Experimental results demonstrate that the proposed algorithm can estimate object depths in a scene reliably and restore the scene radiance efficiently. The rest of the paper is organized as follows. Section 2 describes the haze model, which is employed in this work. Section 3 proposes the static image dehazing algorithm, and Section 4 describes the video dehazing algorithm. Section 5 presents experimental results.
Finally, Section 6 concludes this work.