Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range

Abstract

Images captured under bad weather conditions (e.g., fog, haze, mist, dust, etc.), suffer from poor contrast and visibility, and color distortions. The severity of this degradation depends on the distance, the density of the atmospheric particles and the wavelength. We analyzed eight single image dehazing algorithms representative of different strategies and originally developed for RGB images, over a database of hazy spectral images in the visible range. We carried out a brute force search to find the optimum three wavelengths according to a new combined image quality metric. The optimal triplet of monochromatic bands depends on the dehazing algorithm used and, in most cases, the different bands are quite close to each other. According to our proposed combined metric, the best method is the artificial multiple exposure image fusion (AMEF). If all wavelengths within the range 450–720 nm are used to build a sRGB renderization of the imagaes, the two best-performing methods are AMEF and the contrast limited adaptive histogram equalization (CLAHE), with very similar quality of the dehazed images. Our results show that the performance of the algorithms critically depends on the signal balance and the information present in the three channels of the input image. The capture time can be considerably shortened, and the capture device simplified by using a triplet of bands instead of the full wavelength range for dehazing purposes, although the selection of the bands must be performed specifically for a given algorithm.

SPIDE-Net: Spectral Prior-Based Image Dehazing and Enhancement Network

“…Next, We define three test cases (longer, shorter and best spectral wavelength bands) for performance evaluation and comparisons of proposed method with well-known image dehazing methods. However, the best spectral wavelength band is computed for haze density level ''7'', for the consistency of the previously performed experiments [20]. The proposed method gives favorable results after evaluation and comparison with existing dehazing methods and shows consistent results i.e.…”

“…al algorithm [16] performs better at higher wavelengths [19]. Recently, brute force optimizations is used to find the best wavelength bands triplet as input to different image dehazing methods [20]. Additional sRGB rendered images from SHIA dataset using full visible wavelength bands [52], are utilized for comparison of different methods.…”

“…All the analyses and comparisons proved that existing image dehazing methods are sensitive to spectral bands selection. Moreover, these performance analyses [18], [19], and [20] give reasons for further work on hazy spectral images i.e. additional test cases for performance comparisons are required to analyze the variation of image dehazing methods with spectral bands selection.…”

“…The classifications of image dehazing methods can be made in different ways [4], [5]. First, it can be classified as methods based on physical model [1] using priors [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] [23], [24], [25], [26] or methods using scene statistics without using physical model [6], [7]. Second approach is to classify the algorithms based on image enhancement [8], [9], image fusion [10], [11] and Deep-Learning based methods [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38].…”

“…Recently, a hyper-spectral image database for dehazing called the SHIA (Spectral Hazy Image Database for assessment) [18] is available that contains indoor images captured in controlled haze or fog conditions. Using SHIA database, existing popular image dehazing methods have been compared for performance evaluation using images from different wavelength bands and fog density levels [18], [19], and [20]. Each of the compared image dehazing method performed better at some specific wavelength bands whereas it showed compromised results at other bands.…”