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Poor visibility due to existence of fog and other associated particles in the atmosphere is the most fundamental problem for current vision applications. Recently, techniques for visibility enhancement of images have received a significant attention. However, validation of the existing techniques remains scare due to the lack of balanced distribution on the existing datasets. In this paper, a newly designed dataset entitled "SAMEER-TU Outdoor Dataset" is proposed. The dataset contains 5880 images of urban scenes in fog, poor illumination and clear conditions. Also, ground truths are provided in terms of meteorological weather parameters and corresponding clear scene images of the degraded images. On the designed dataset, quantitative analysis of existing visibility enhancement techniques (i. e., conventional and deep learning techniques) are performed based on qualitative evaluation metrics. It comes as no surprise that the existing visibility enhancement techniques and there still existing significant for further improvement.