Ghost-Free Image Using Blur and Noise Estimation

Abstract

This paper presents an efficient image enhancement method by fusion of two different exposure images in low-light condition. We use two degraded images with different exposures: one is a long-exposure image that preserves the brightness but contains blur and the other is a short-exposure image that contains a lot of noise but preserves object boundaries. The weight map used for image fusion without artifacts of blur and noise is computed using blur and noise estimation. To get a blur map, edges in a long-exposure image are detected at multiple scales and the amount of blur is estimated at detected edges. Also, we can get a noise map by noise estimation using a denoised short-exposure image. Ghost effect between two successive images is avoided according to the moving object map that is generated by a sigmoid comparison function based on the ratio of two input images. We can get result images by fusion of two degraded images using the weight maps. The proposed method can be extended to high dynamic range imaging without using information of a camera response function or generating a radiance map. Experimental results with various sets of images show the effectiveness of the proposed method in enhancing details and removing ghost artifacts.