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In the second stage, the dark channel prior method is used to estimate the transmission and the atmospheric light.
With the estimated hazy image I ′ from the first stage, the dark channel prior method is then used to estimate the transmission and the atmospheric light.
This dark channel prior method is a major breakthrough for haze removal from a single image and is the state of the art until now.
Since real-life images often have a degree of noise and blur coming from the imaging sensor, the dark channel prior method is effectively invalid.
The reason for this may be as follows: Because of the blur interference, the dark channel prior method is used to remove haze directly, which results in the halo artifacts in DC.
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Therefore, the recovery results (DC) by the dark channel prior method are relatively poor.
Another discovery was that a new dark prior method was created using Lemma 2. A cost function was designed to minimize the average centroid position while staying within the atmospheric dichromatic model.
Neither of these prior methods is capable of estimating isoform expression levels, and thus we compared only gene expression estimates.
The identification results of the proposed and prior methods are compared.
The improvements offered by MoRFpred, when compared to prior methods, are due to the use of large dataset and novel architecture that combines SVM-based predictions with alignment and which uses a comprehensive and well designed feature-based sequence representation.
For generating the defogged image using the centroid prior, x ̂ C, a gamma value of 1/2 was used for the examples in this article, e.g., x ̂ C 1 / 2. The complete algorithm for the ellipsoid prior defogging method is in Algorithm 5.
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