Multiexposure photomontage with handheld cameras Abstract   The paper studies the image fusion from multiple images taken by handheld cameras with different exposures. Existing methods often generate unsatisfactory results, such as blurringghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriately fusion of local regions (e.g., over or under exposed). In addition, they often require a highquality image registration, which is hard to achieve in scenarios with large depth variations and dynamic textures, and is also timeconsuming. In this paper, we propose to enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, the method first uses a Markov Random Field (MRF) energy for the labeling of all pixels, which assigns different labels to different aligned input images. During the labeling, it chooses wellexposured regions and skips moving objects at the same time. Then, the proposed method combines a Laplacian image according to the labels and constructs the fusion result by solving the Poisson equation. Furthermore, it adds some internal constraints when solving the Poisson equation for balancing and improving fusion results. We present various challenging examples, including staticdynamic, indooroutdoor and daytimenighttime scenes, to demonstrate the effectiveness and practicability of the proposed method.

Camera Exposure Control for Robust Robot Vision with NoiseAware Image Quality Assessment In this paper, we propose a noiseaware exposure control algorithm for robust robot vision. Our method aims to capture bestexposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures lightweight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a realtime and fully automatic exposure and gain control technique based on the NelderMead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.