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 FI-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. We can employ our enhancement method in replacement of RGB images for classifier training e.g., CNN architectures, which in turn can lead to more accurate classification. Code will be released for public use.