Existing multi-scale exposure fusion (MEF) algorithms cannot preserve relative brightness in an image fused from two large-exposure-ratio images if high-light regions in the dark image are darker than shadow regions in the bright image. In this letter, a strategy by synthesizing a virtual image in medium exposure is presented to brighten the high-light regions in the dark image and darken the darkest regions in the bright image. The virtual image is generated via intensity mapping functions (IMFs). In order to avoid possible color distortion in the virtual image due to one-to-many mapping, two intermediate virtual images with the same exposure time are generated by the two input images, and then merged together to produce the final virtual image using properly defined weights. The final image is obtained by fusing the original two input images and the virtual image via a state-of-the-art MEF algorithm. Experimental results show that the relative brightness is preserved better by the proposed algorithm and the MEF-SSIM is significantly improved by the proposed method.

Image texture

This paper presents a novel image enhancement approach to avoid common artifacts such as over-exposure, color cast, and unnatural results. The key innovation lies in estimating the illumination map of an underexposed image using a sparse bright channel. Our approach includes an algorithm that enforces the sparsity of the inverted bright channel, enabling the indirect estimation of a coarse but suitable initial illumination map. This initial map is refined using an updated weight-constrained regularization with joint local exposure and detail feedback constraints, producing a piece-wise smooth, structure-preserving illumination map. Computer simulations show that the proposed method is competitive with or even outperforms several state-of-the-art enhancement methods in terms of both subjective and objective evaluations.

In this letter, a new texture filter with scale-aware gradients and structural preservation is proposed. The proposed filter uses a hybrid $L_{0}$ - $H^{-1} $ variational model via measuring sparsity with scale-aware gradients by the $L_{0}$ norm and structural fidelity by the $H^{-1}$ norm with the embedded Laplacian operator. Extensive qualitative and quantitative experimental results demonstrate that the proposed method 1) smooths small-scale low/high contrast textures and intensive noise while preserving sharp and prominent structures simultaneously; 2) significantly outperforms state-of-the-art texture filtering methods; and 3) has fast convergence.