  Laparoscopic surgery has a limited field of view. Laser ablation in a
laproscopic surgery causes smoke, which inevitably influences the surgeon's
visibility. Therefore, it is of vital importance to remove the smoke, such that
a clear visualization is possible. In order to employ a desmoking technique,
one needs to know beforehand if the image contains smoke or not, to this date,
there exists no accurate method that could classify the smoke/non-smoke images
completely. In this work, we propose a new enhancement method which enhances
the informative details in the RGB images for discrimination of smoke/non-smoke
images. Our proposed method utilizes weighted least squares optimization
framework~(WLS). For feature extraction, we use statistical features based on
bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of
Gaussian~(LoG). We then train a SVM classifier with binary smoke/non-smoke
classification task. We demonstrate the effectiveness of our method on Cholec80
dataset. Experiments using our proposed enhancement method show promising
results with improvements of 4\% in accuracy and 4\% in F1-Score over the
baseline performance of RGB images. In addition, our approach improves over the
saturation histogram based classification methodologies Saturation
Analysis~(SAN) and Saturation Peak Analysis~(SPA) by 1/5\% and 1/6\% in
accuracy/F1-Score metrics.
