Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

Simultaneous Stereo Video Deblurring and Scene Flow Estimation

Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods. We evaluate our method extensively on two available datasets and achieve significant improvement in flow estimation and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 201

Gradient-Based Correction of Chromatic Aberration in the Joint Acquisition of Color and Near-Infrared Images

Chromatic aberration distortions, such as wavelength-dependent blur caused by imperfections in photographic lenses, have long been studied in color imaging. The problem becomes more challenging to solve in the case of color and near-infrared joint acquisition, as a wider range of wavelengths is captured. In this paper, we assume that the color image is in focus, hence, the NIR image captured with the same focus settings is blurred. We propose an algorithm that estimates the blur kernel and deblurs the NIR image, using the in-focus color image as a guide in both steps. In the deblurring step, we retrieve the lost details of the NIR image by using the sharp edges of the color image. The main diculty in this task is caused by the fact that the edges of color and NIR images are not always correlated. To handle this issue, the algorithm analyzes the dierences between the gradients of NIR and color channels. Simulation results verify the eectiveness of our algorithm, both in estimating the blur kernel and deblurring the NIR image, without producing ringing artifacts inherent to the results of other deblurring methods

Generalising the ideal pinhole model to multi-pupil imaging for depth recovery

This thesis investigates the applicability of computer vision camera models in recovering depth information from images, and presents a novel camera model incorporating a modified pupil plane capable of performing this task accurately from a single image. Standard models, such as the ideal pinhole, suffer a loss of depth information when projecting from the world to an image plane. Recovery of this data enables reconstruction of the original scene as well as object and 3D motion reconstruction. The major contributions of this thesis are the complete characterisation of the ideal pinhole model calibration and the development of a new multi-pupil imaging model which enables depth recovery. A comprehensive analysis of the calibration sensitivity of the ideal pinhole model is presented along with a novel method of capturing calibration images which avoid singularities in image space. Experimentation reveals a higher degree of accuracy using the new calibration images. A novel camera model employing multiple pupils is proposed which, in contrast to the ideal pinhole model, recovers scene depth. The accuracy of the multi-pupil model is demonstrated and validated through rigorous experimentation. An integral property of any camera model is the location of its pupil. To this end, the new model is expanded by generalising the location of the multi-pupil plane, thus enabling superior flexibility over traditional camera models which are confined to positioning the pupil plane to negate particular aberrations in the lens. A key step in the development of the multi-pupil model is the treatment of optical aberrations in the imaging system. The unconstrained location and configuration of the pupil plane enables the determination of optical distortions in the multi-pupil imaging model. A calibration algorithm is proposed which corrects for the optical aberrations. This allows the multi-pupil model to be applied to a multitude of imaging systems regardless of the optical quality of the lens. Experimentation validates the multi-pupil model’s accuracy in accounting for the aberrations and estimating accurate depth information from a single image. Results for object reconstruction are presented establishing the capabilities of the proposed multi-pupil imaging model

Camera based Display Image Quality Assessment

This thesis presents the outcomes of research carried out by the PhD candidate Ping Zhao during 2012 to 2015 in Gjøvik University College. The underlying research was a part of the HyPerCept project, in the program of Strategic Projects for University Colleges, which was funded by The Research Council of Norway. The research was engaged under the supervision of Professor Jon Yngve Hardeberg and co-supervision of Associate Professor Marius Pedersen, from The Norwegian Colour and Visual Computing Laboratory, in the Faculty of Computer Science and Media Technology of Gjøvik University College; as well as the co-supervision of Associate Professor Jean-Baptiste Thomas, from The Laboratoire Electronique, Informatique et Image, in the Faculty of Computer Science of Universit´e de Bourgogne. The main goal of this research was to develop a fast and an inexpensive camera based display image quality assessment framework. Due to the limited time frame, we decided to focus only on projection displays with static images displayed on them. However, the proposed methods were not limited to projection displays, and they were expected to work with other types of displays, such as desktop monitors, laptop screens, smart phone screens, etc., with limited modifications. The primary contributions from this research can be summarized as follows: 1. We proposed a camera based display image quality assessment framework, which was originally designed for projection displays but it can be used for other types of displays with limited modifications. 2. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact, which is mainly introduced by the camera lens. 3. We proposed a method to optimize the camera’s exposure with respect to the measured luminance of incident light, so that after the calibration all camera sensors share a common linear response region. 4. We proposed a marker-less and view-independent method to register one captured image with its original at a sub-pixel level, so that we can incorporate existing full reference image quality metrics without modifying them. 5. We identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays, and we used the proposed framework to evaluate the prediction performance of the state-of-the-art image quality metrics regarding these attributes. The proposed image quality assessment framework is the core contribution of this research. Comparing to conventional image quality assessment approaches, which were largely based on the measurements of colorimeter or spectroradiometer, using camera as the acquisition device has the advantages of quickly recording all displayed pixels in one shot, relatively inexpensive to purchase the instrument. Therefore, the consumption of time and resources for image quality assessment can be largely reduced. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact primarily introduced by the camera lens. We used a hazy sky as a closely uniform light source, and the vignetting mask was generated with respect to the median sensor responses over i only a few rotated shots of the same spot on the sky. We also proposed a method to quickly determine whether all camera sensors were sharing a common linear response region. In order to incorporate existing full reference image quality metrics without modifying them, an accurate registration of pairs of pixels between one captured image and its original is required. We proposed a marker-less and view-independent image registration method to solve this problem. The experimental results proved that the proposed method worked well in the viewing conditions with a low ambient light. We further identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays. Subsequently, we used the developed framework to objectively evaluate the prediction performance of the state-of-art image quality metrics regarding these attributes in a robust manner. In this process, the metrics were benchmarked with respect to the correlations between the prediction results and the perceptual ratings collected from subjective experiments. The analysis of the experimental results indicated that our proposed methods were effective and efficient. Subjective experiment is an essential component for image quality assessment; however it can be time and resource consuming, especially in the cases that additional image distortion levels are required to extend the existing subjective experimental results. For this reason, we investigated the possibility of extending subjective experiments with baseline adjustment method, and we found that the method could work well if appropriate strategies were applied. The underlying strategies referred to the best distortion levels to be included in the baseline, as well as the number of them