Bilateral filters suffer from halo artifacts when they are applied for image enhancement. In this paper, a new bilateral filter is proposed in gradient domain to address this problem. Both spatial similarity parameter and intensity similarity parameter of the proposed filter are spatially varying instead of being fixed as in the existing bilateral filters. As a result, it can preserve edges and smooth flat areas better than the existing bilateral filters. The proposed filter is then adopted to design a selectively detail-enhanced exposure fusion algorithm. Fine details of multiple differently exposed images are extracted simultaneously using the proposed filter. Instead of amplifying and adding all extracted fine details to an intermediate image which is fused by an existing exposure fusion algorithm, the fine details in all areas except flat ones are amplified and added to the intermediate image. The resultant algorithm can reduce halo artifacts and prevent noise in flat areas from being amplified in the final image. Therefore, the proposed algorithm fuses images with much better visual quality.
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