Multiple-filtering process and its application in edge detection

In this paper, a procedure of edge detection for a high dynamic range image with damaged edge information is proposed. This procedure is based on a scheme of multiple filtering processes which does not include any segmentation of the image. Three different filtering processes are designed to generate three gradient maps, in each of which gradients are calculated and modulated by using a specific filter. The enhanced gradients, i.e. those modulated correctly, are identified in each of the three gradient maps by using a selection algorithm. They are taken to generate a complete edge map. This procedure allows varieties of edge gradient enhancements applied in the same image by employing a set of simple filters without segmentation. The effectiveness of the detection process has been confirmed by simulations

Exposure Fusion Using Boosting Laplacian Pyramid

Abstract-This paper proposes a new exposure fusion approach for producing a high quality image result from multiple exposure images. Based on the local weight and global weight by considering the exposure quality measurement between different exposure images, and the just noticeable distortion-based saliency weight, a novel hybrid exposure weight measurement is developed. This new hybrid weight is guided not only by a single image's exposure level but also by the relative exposure level between different exposure images. The core of the approach is our novel boosting Laplacian pyramid, which is based on the structure of boosting the detail and base signal, respectively, and the boosting process is guided by the proposed exposure weight. Our approach can effectively blend the multiple exposure images for static scenes while preserving both color appearance and texture structure. Our experimental results demonstrate that the proposed approach successfully produces visually pleasing exposure fusion images with better color appearance and more texture details than the existing exposure fusion techniques and tone mapping operators. Index Terms-Boosting Laplacian pyramid, exposure fusion, global and local exposure weight, gradient vector

Multimodal enhancement-fusion technique for natural images.

Masters Degree. University of KwaZulu-Natal, Durban.This dissertation presents a multimodal enhancement-fusion (MEF) technique for natural images. The MEF is expected to contribute value to machine vision applications and personal image collections for the human user. Image enhancement techniques and the metrics that are used to assess their performance are prolific, and each is usually optimised for a specific objective. The MEF proposes a framework that adaptively fuses multiple enhancement objectives into a seamless pipeline. Given a segmented input image and a set of enhancement methods, the MEF applies all the enhancers to the image in parallel. The most appropriate enhancement in each image segment is identified, and finally, the differentially enhanced segments are seamlessly fused. To begin with, this dissertation studies targeted contrast enhancement methods and performance metrics that can be utilised in the proposed MEF. It addresses a selection of objective assessment metrics for contrast-enhanced images and determines their relationship with the subjective assessment of human visual systems. This is to identify which objective metrics best approximate human assessment and may therefore be used as an effective replacement for tedious human assessment surveys. A subsequent human visual assessment survey is conducted on the same dataset to ascertain image quality as perceived by a human observer. The interrelated concepts of naturalness and detail were found to be key motivators of human visual assessment. Findings show that when assessing the quality or accuracy of these methods, no single quantitative metric correlates well with human perception of naturalness and detail, however, a combination of two or more metrics may be used to approximate the complex human visual response. Thereafter, this dissertation proposes the multimodal enhancer that adaptively selects the optimal enhancer for each image segment. MEF focusses on improving chromatic irregularities such as poor contrast distribution. It deploys a concurrent enhancement pathway that subjects an image to multiple image enhancers in parallel, followed by a fusion algorithm that creates a composite image that combines the strengths of each enhancement path. The study develops a framework for parallel image enhancement, followed by parallel image assessment and selection, leading to final merging of selected regions from the enhanced set. The output combines desirable attributes from each enhancement pathway to produce a result that is superior to each path taken alone. The study showed that the proposed MEF technique performs well for most image types. MEF is subjectively favourable to a human panel and achieves better performance for objective image quality assessment compared to other enhancement methods

Multiple-filtering-process for the edge detection of high-dynamic-range Images

Edge detection is a basic image processing operation usually used in the first stage of the complex image processing systems, such as restoration, and its quality has a direct effect on the performance of the systems. The extraction of correct edges from a noise-contaminated image or an image with severe deformation is a challenging task. The objective of the work of this thesis is to develop an edge detection method to extract effectively edge signals from the images with the edge information seriously damaged while being acquired in high dynamic range (HDR) scenes. To achieve the objective, an edge detection method based on a multiple-high-pass-filtering process scheme has been proposed. Each of the filtering processes is designed to suit one of the signal deformation conditions, and is applied to the entire input image, instead of the designated regions, in order to spare the computation of image segmentation. A fusion process is then performed to merge the gradient maps generated by the multiple filtering processes into one. A detection procedure has been designed for a typical case of HDR images acquired with three different kinds of deformations due to the non-ideal characteristics of acquisition device. Based on the study of the characteristics, three high-pass filtering processes are designed to generate gradient signals with different modulations. A simple selection algorithm is developed for an easy fusion process. The results of the simulation with different types of HDR images have shown that, compared to some of most commonly used detection processes, the proposed one leads to a better quality of edge signals from severely deformed HDR images