Segmentation-assisted detection of dirt impairments in archived film sequences

A novel segmentation-assisted method for film dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection for a wide variety of test material

Stellar Double Coronagraph: a multistage coronagraphic platform at Palomar observatory

We present a new instrument, the "Stellar Double Coronagraph" (SDC), a flexible coronagraphic platform. Designed for Palomar Observatory's 200" Hale telescope, its two focal and pupil planes allow for a number of different observing configurations, including multiple vortex coronagraphs in series for improved contrast at small angles. We describe the motivation, design, observing modes, wavefront control approaches, data reduction pipeline, and early science results. We also discuss future directions for the instrument.Comment: 25 pages, 12 figures. Correspondence welcome. The published work is open access and differs trivially from the version posted here. The published version may be found at http://iopscience.iop.org/article/10.1088/1538-3873/128/965/075003/met

Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs

Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images

Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms

Multistage Filtering Algorithm for Salt and Pepper Noise Removal from Highly Corrupted Microscopic Blood Images

Image quality mainly affects the performance of anymedical image processing system. Salt & pepper noise is onetype of noise that significantly degrades the image quality. Suchnoise can be frequently encountered in digital microscopicimages due to technical reasons. Moreover, high ratios of Salt &pepper noise make the image excessively corrupted orunreadable. Standard and modified median filters can usuallyhandle low/medium Salt & Pepper noise densities, mostly at theexpense of edge/details preservation. However, they totally failfor highly corrupted images where noise density reaches 90%.In this paper, we present a new multistage filtering algorithmfor Salt & pepper noise reduction from highly corrupted imageswhile preserving image details and edges as better as possible.The proposed algorithm includes two filtering stages throughwhich image is firstly de-noised via utilizing adaptive medianfilter then decision based median filter. Our multistage filterhas been successfully applied on noisy microscopic blood imagesobtained from Malaria-infected blood smears. Results revealthat the presented filtering algorithm outperforms standard andmodified median algorithms in terms of PSNR, MSE and IEFvalues, specifically for images with more than 80% of salt &pepper noise. This indicates that using our multistage filteringalgorithm against high Salt & Pepper noise densities, does notonly remove the noise effectively but also achieves a better edgeand details preservation, hence a better image enhancement