This work proposes strategies and solutions to tackle the problem of building photo-mosaics of very large underwater optical surveys, presenting contributions to the image preprocessing, enhancing and blending steps, and resulting in an improved visual quality of the final photo-mosaic. The text opens with a comprehensive review of mosaicing and blending techniques, before proposing an approach for large scale underwater image mosaicing and blending. In the image preprocessing step, a depth dependent illumination compensation function is used to solve the non-uniform illumination appearance due to light attenuation. For image enhancement, the image contrast variability due to different acquisition altitudes is compensated using an adaptive contrast enhancement based on an image quality reference selected through a total variation criterion. In the blending step, a graph-cut strategy operating in the image gradient domain over the overlapping regions is suggested. Next, an out-of-core blending strategy for very large scale photo-mosaics is presented and tested on real data. Finally, the performance of the approach is evaluated and compared with other approaches.

Image Blending Techniques and their Application in Underwater Mosaicing

Small-scale unmanned aerial vehicles (UAVs) are an emerging research area and have been recently demonstrated in many applications including disaster response management, construction site monitoring and wide area surveillance where multiple UAVs impose various benefits. In this work we present a system composed of multiple networked UAVs for autonomously monitoring a wide area scenario. Each UAV is able to follow waypoints and capture high-resolution images. In order to overcome the strong resource limitations we implement an incremental approach for generating an orthographic mosaic from the individual images. Captured images are pre-processed on-board, annotated with other sensor data and transferred by a prioritized transmission scheme. The ultimate goal of our approach is to generate an overview mosaic as fast as possible and to improve its quality over time. The mosaicking exploits position and orientation data of the UAV to compute rough image projections which are incrementally refined by scene structure analysis when more image data is available. We evaluate our incremental mosaicking in the strongly resource limited UAV network composed of up to three concurrently flying UAVs. Our results are compared to state-of-the-art mosaicking methods and show a unique performance in our dedicated application scenarios.

Resource aware and incremental mosaics of wide areas from small-scale UAVs

The paper presents an effective nonlinear adaptive switching non-local filter for the restoration of impulse-corrupted digital images by using distinct impulse detection and correction stages. The correction scheme of the filter adaptively switches between details-preserving non-local mode and signal restoration-based local mode to facilitate high fidelity in the restored image. The non-local filtering operation replaces impulses with a remote pixel that better suits the local image conditions. The algorithm works in this non-local mode only when there are sufficient uncorrupted pixels in the local neighborhood of the corrupted pixel to be replaced. Otherwise, the algorithm replaces impulsive pixels with the median of the uncorrupted pixels from the local neighborhood. Experimental results from various impulse noise levels support the improved performance of the proposed algorithm over other algorithms both subjectively and objectively.

Adaptive Switching Non-local Filter for the Restoration of Salt and Pepper Impulse-Corrupted Digital Images

Unmanned Aerial Vehicles (UAVs) have been widely used to acquire high quality terrain images of the areas of interest, particularly when such a task could potentially risk human life or even impossible as the areas cannot be accessed easily by surveyors. Once the images have been obtained, traditional photogrammetric processing process can be used to establish a relative orientation model and then, absolute orientation model with the procedures of space resection and intersection. In many such applications, the geo- referenced images which are stitched together to represent the geospatial relationships for the feature objects are sufficient. A fast or near real-time processing approach for UAV images using GPS/INS data has being investigated for years. One beneficial application of such approach is the capability of quick production of geo-referenced images for various engineering or business activities, such as urban and road planning, the site selection of factories and bridges, etc. In this paper, we have proposed a new fast processing approach for the UAV images collected with an integrated GPS/INS/Vision system. The approach features that the corresponding points between images have been determined, and then coordinate transformation is carried out to implement image stitching. The accuracy of corresponding points normally affects the quality of stitched images, but the results of our experiments revealed that the image stitching errors were obvious even the accuracy of corresponding points was high. The stitching errors could be caused by the changes of surface elevation.

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A new approach to fast mosaic uav images

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well.

http://www.jmss.mui.ac.ir/index.php/jmss/article/download/62/132

Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

An approach of image mosaic based on binary image region matching is presented and applied in remote sensing serial image processing. Adopted the nearest-neighbour interpolation, the original images are rectified on the basis of the values of external orientation elements of the camera installed on the unmanned aerial vehicle (UAV). Image matching includes two procedures, i.e., rough matching and fine matching. In rough matching, the area coverage of overlapped regions between two consecutive images to be stitched is determined approximately at first. Both of the overlapped regions of the two images are processed to binary image, and then the binary images are filtered by mathematic morphologic method. In the appointed binary image region of the former image, feature template is searched and extracted. Through the XOR operation of the feature template and search region, some possible matching positions in the overlapped region of the latter image are obtained. In the fine matching, the sequential similarity detection algorithm (SSDA) is adopted to perform matching computation in the some small regions near the positions extracted in the rough matching, and then the relative position offsets in X-orientation and Y-orientation between the two consecutive images are obtained. Based on the result of the image matching, the two images are stitched. Experimental results by simulation show that the approach presented in the paper greatly improves the operation speed, while the precision remains fine, than it can be applied in real-time mosaicking of serial images.

A Fast Mosaic Approach for Remote Sensing Images

A new method of image denoising was put forward, based on integrated utilization of Curvelet transform and Cycle spinning. Because there existed "encircling" phenomenon for Curvelet transform, if images threshold denoising of Curvelet coefficients was performed directly, the image would be distorted, in this paper, Cycle spinning was introduced to eliminate the image distortion produced by "encircling". The simulation experiment indicated that the algorithm had perfect inhibiting ability for noise, moreover, it can protect the detail information of image; when different kinds of impulse noise images with different density were filtered, satisfied results can always be obtained.

Yanping Li

Papers

A Fast Mosaic Approach for Remote Sensing Images

Image Denoising Method Based on Curvelet Cycle Spinning