Real-time Model-based Image Color Correction for Underwater Robots

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Joint Correcting and Refinement for Balanced Low-Light Image Enhancement

Low-light image enhancement tasks demand an appropriate balance among brightness, color, and illumination. While existing methods often focus on one aspect of the image without considering how to pay attention to this balance, which will cause problems of color distortion and overexposure etc. This seriously affects both human visual perception and the performance of high-level visual models. In this work, a novel synergistic structure is proposed which can balance brightness, color, and illumination more effectively. Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement. Stage 1: we utilize a basic encoder-decoder and local supervision mechanism to extract local information and more comprehensive details for enhancement. Stage 2: cross-stage feature transmission and spatial feature transformation further facilitate color correction and feature refinement. Stage 3: we employ a dynamic illumination adjustment approach to embed residuals between predicted and ground truth images into the model, adaptively adjusting illumination balance. Extensive experiments demonstrate that the proposed method exhibits comprehensive performance advantages over 21 state-of-the-art methods on 9 benchmark datasets. Furthermore, a more persuasive experiment has been conducted to validate our approach the effectiveness in downstream visual tasks (e.g., saliency detection). Compared to several enhancement models, the proposed method effectively improves the segmentation results and quantitative metrics of saliency detection. The source code will be available at https://github.com/woshiyll/JCRNet

Axial range of conjugate adaptive optics in two-photon microscopy

We describe an adaptive optics technique for two-photon microscopy in which the deformable mirror used for aberration compensation is positioned in a plane conjugate to the plane of the aberration. We demonstrate in a proof-of-principle experiment that this technique yields a large field of view advantage in comparison to standard pupil-conjugate adaptive optics. Further, we show that the extended field of view in conjugate AO is maintained over a relatively large axial translation of the deformable mirror with respect to the conjugate plane. We conclude with a discussion of limitations and prospects for the conjugate AO technique in two-photon biological microscopy

Medical Image Contrast Enhancement via Wavelet Homomorphic Filtering Transform

A novel enhancement algorithm for magnetic resonance (MR) images based on spatial homomorphic filtering transform is proposed in this paper. By this method, the source image is decomposed into different sub-images by dyadic wavelet transform. Homomorphic filtering functions are applied in performing filtering of corresponding sub-band images to attenuate the low frequencies as well as amplify the high frequencies, and a linear adjustment is carried out on the low frequency of the highest level. Later, inverse dyadic wavelet transform is applied to reconstruct the object image. Experiment results on MR images illustrate that the proposed method can eliminate non-uniformity luminance distribution effectively, some subtle tissues can be improved effectually, and some weak sections have not been smoothed by the novel method.