High Dynamic Range Image Generation Based on Convolutional Weight Optimization Robust to Mixed Noise Removal
Exposure blending generates a high dynamic range (HDR) image from multiple exposure images. However, when photographing in a low light scene, these images are deteriorated due to various types of noise, and then it brings down the dynamic range. Existing methods based on pixel wise blending cannot sufficiently reduce noise, especially in the case of a few input images. This report proposes a convolutional weight optimization method for exposure blending that robustly removes mixed noise and under/over exposed pixels with a few inputs. In blending each pixel, the proposed method convolves neighboring pixels and generates a noise free HDR image. The convolution of local regions enables to enhance the denoising capability of image blending. To find a set of weight maps for convolution, we introduce a weight optimization problem as a convex optimization problem, in which Huber loss function is utilized as a fidelity measure in blending to make the method robust to outliers, and solve the optimization problem by using the primal dual splitting method. The weighted sum of the noisy input images with the estimated weight maps makes a noise free HDR image. Experimental results show the validity of the proposed method compared with several conventional methods.
2018 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)