Conventional Restoration Methods Research Articles

Color Channel Compensation (3C): A fundamental pre-processing step for image enhancement.

This article introduces a novel solution to improve image enhancement in terms of color appearance. Our approach, called Color Channel Compensation (3C), overcomes artifacts resulting from the severely non-uniform color spectrum distribution encountered in images captured under hazy night-time conditions, underwater, or under non-uniform artificial illumination. Our solution is founded on the observation that, under such adverse conditions, the information contained in at least one color channel is close to completely lost, making the traditional enhancing techniques subject to noise and color shifting. In those cases, our pre-processing method proposes to reconstruct the lost channel based on the opponent color channel. Our algorithm subtracts a local mean from each opponent color pixel. Thereby, it partly recovers the lost color from the two colors (red-green or blue-yellow) involved in the opponent color channel. The proposed approach, whilst simple, is shown to consistently improve the outcome of conventional restoration methods. To prove the utility of our 3C operator, we provide an extensive qualitative and quantitative evaluation for white balancing, image dehazing, and underwater enhancement applications.

Comprehensive approach to restoring urban recreational reservoirs. Part 1 – Reduction of nutrient loading through low-cost and highly effective ecohydrological measures

The study presented in this paper aimed to improve the water quality of a small, stormwater-fed urban river and a cascade of ponds and reservoirs (Łódź, Poland) that are intensively used for recreation. We tested a combination of conventional restoration methods (bottom sediment removal and biomanipulation) and comprehensive ecohydrological restoration methods (hybrid systems, sequential sedimentation-biofiltration systems (SSBS), floating islands, landform-adjusted shoreline vegetation and plant harvesting). As a result of sediment removal (9795 m3, cost: €80761), almost 12.5 tons of nitrogen and 197 kg of phosphorous were eliminated from the reservoirs. The hybrid systems and SSBSs reduced the nutrient transport and suspended solid levels by 49%–98.5% and 89.6%–98.6%, respectively, from the upstream catchments to the reservoirs (investment cost: €63929). Generally, restoration improved most of the water quality indicators, although an increase in the phosphate concentration was observed from 0.06 to 0.17 mg L−1 before restoration to 0.13–0.28 mg L−1 immediately after restoration as a result of disturbances resulting from bottom sediment removal. Four years after restoration, the concentrations of the total forms of nutrients and ammonium were still considerably lower than before restoration. The number of summer days with microcystins and with chlorophyll a concentrations above the WHO limits for recreational waters decreased. However, there was a gradual decline in the physicochemical parameters of the water due to the local impacts of human use (swimming and duck and fish feeding), rather than from other external or internal loads, which were effectively controlled.

An Automatic Calculation Method of MTF and the Application in Blurred Images Restoration

Modulation transfer function (MTF) is a comprehensive index for the objective reflection of the quality of the imaging systems. In the field of image processing, the slanted-edge method is usually adopted to compute MTF for images. However, the sub-images with slanted edges are extracted from original image by subjective judgment and manually operation, which is bound to lead to inefficiency of calculation, low accuracy and instability of results with the participation of humans. Aiming at the above problem, this paper presents an automatic MTF calculation method for blurred images and applies it to the process of image restoration by developing a two-dimensional MTF filter and utilizing it into conventional restoration methods such as the Wiener filtering, least squares filtering and Lucy-Richardson algorithm. Experiment results indicate the proposed method achieved an automatic, fast and accurate MTF computation for blurred images, and MTF-based restoration methods were superior to traditional ones in restoration effects.

Super-Resolution Iris Image Restoration using Single Image for Iris Recognition

Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.

New protocol for construction of eyeglasses-supported provisional nasal prosthesis using CAD/CAM techniques

A new protocol for making an immediate provisional eyeglasses-supported nasal prosthesis is presented that uses laser scanning, computer-aided design/computer-aided manufacturing procedures, and rapid prototyping techniques, reducing time and costs while increasing the quality of the final product. With this protocol, the eyeglasses were digitized, and the relative position of the nasal prosthesis was planned and evaluated in a virtual environment without any try-in appointment. This innovative method saves time, reduces costs, and restores the patient's aesthetic appearance after a disfiguration caused by ablation of the nasal pyramid better than conventional restoration methods. Moreover, the digital model of the designed nasal epithesis can be used to develop a definitive prosthesis anchored to osseointegrated craniofacial implants.

Restauración atraumática para el control de la caries dental: historia, características y aportes de la técnica

This paper presents relevant scientific information on the history, characteristics, and contributions of atraumatic restorative treatment (ART) for use in preventing and controlling dental caries. Within the area of oral public health, ART has been for years an economical, effective method for preventing and controlling caries in vulnerable populations. Among other things, ART reduces the stress and anxiety in patients that conventional restoration methods produce. This technique promises major benefits for Latin America. However, given its limitations with dental cavities on two or more surfaces, it is recommended that more research on this approach be encouraged, with the aim of improving the technique's effectiveness based on its characteristics, indications, and technical merits.

A restoration method of image sequences which are corrupted by blotches

AbstractThe digitization and restoration of images that have been degraded by white or black blotches seen in motion picture films prior to the 1950s have attracted attention. Conventional restoration methods are composed of two processes. The first process is motion estimation by block matching (BM). The second process matches the current frame to the previous and next frames based on the motion estimates and filters in the temporal direction. Blotch detection accompanies the filtering in the second process. The BM used in the first process is average BM or sorted BM. Blotch detection in the second process employs Spike Detection Index‐a (SDIa). In conventional BM, however, there are limits to the accuracy of motion estimation when blotches are present in the blocks used to estimate the motion in images corrupted by blotches. Furthermore, the accuracy of the filtering in the second process is affected by the accuracy of the motion estimation. And although blotch detection based on SDIa is a simple method, the accuracy of its blotch detection is a problem. To solve these problems in this paper, we create a blotch position image by using a median filter with an iteration constraint as the first stage, and obtain a virtual original image with the blotches removed by filtering based on the blotch position image. The accuracy of the motion estimation is improved by using the virtual original image, and the accuracy of the filtering is improved by using the blotch position image in the filtering. Based on these innovations, we propose a new method to obtain a restored image sequence having enhanced image quality. The effectiveness of the proposed method is clearly demonstrated through a variety of examples. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(10): 8–18, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1121

Restoring images using a feed-forward neural network and the gradient adaptive learning rate algorithm

The problem of restoring high-quality images from degraded imaging systems without any prior knowledge of the degradation model and statistics of noise is considered. A novel training algorithm, the gradient adaptive learning rate, is employed to train a feedforward neural network, the nonlinear restoration model. A linearization model of the neuron's sigmoidal activation function is utilized to speed up the convergence of the algorithm. Restoration is accomplished by making use of the generalization capabilities of the network. Ideal data sets representing flat and edge regions that would be occurring in real images have been proposed to train the network while real images have been used only during operation of the system. Computer simulation examples are given to illustrate the significance of this method. Comparison with one of the conventional restoration methods is also presented. Simulation results indicate better performance of the proposed method to other competing methods.