Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images

Abstract

In this letter, a surround suppression variational Retinex enhancement algorithm (SSVR) is proposed for non-uniform illumination images. Instead of a gradient module, a surround suppression mechanism is used to provide spatial information in order to constrain the total variation regularization strength of the illumination and reflectance. The proposed strategy preserves the boundary areas in the illumination so that halo artifacts are prevented. It also preserves textural details in the reflectance to prevent from illumination compression, which further contributes to the contrast enhancement in the resulting image. In addition, strong regularization strength is enforced to eliminate uneven intensities in the homogeneous areas. The split Bregman optimization algorithm is employed to solve the proposed model. Finally, after decomposition, a contrast gain is added to reflectance for contrast enhancement, and a Laplacianbased gamma correction is added to illumination for prevent color cast. The recombination of the modified reflectance and illumination become the final result. Experimental results demonstrate that the proposed SSVR algorithm performs better than other methods.

Single image dehazing and denoising combining dark channel prior and variational models

“…Owing to the complexity of computation, we also design their fast split Bregman algorithm [15]. Additionally, variational Retinex (VR) models [16] have also been applied to image dehaze by some researchers [17][18][19]. In order to show the merits of our proposed models in denoising effect, edge-preserving properties, and computation efficiency, both simulated images and real nature images taken from the challenging scenes are used to achieve competitive results.…”