Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.

Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting

The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens.As a result,it is difficult to use the obtained image information.To make use of the effective information in the image,these distorted imagesmust first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities.To solve this problem,this study presents an adaptive classification fitting method for fish-eye image correction.The degree of distortion in the image is represented by the difference value of the distances fromthe distorted point and undistorted point to the center of the image.The target points selected in the image are classified by the difference value.In the areas classified by different distortion differences,different parameter curves were used for fitting and correction.The algorithm was verified through experiments.The results showed that this method has a substantial correction effect on fish-eye images taken by different fish-eye lenses.

Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition.

Image Distortion of Optical Coherence Tomography

A kind of image distortion in Optical Coherence Tomography (OCT) resulted from average refractive index changes between structures of bio tissue is discussed for the first time.Analysis is given on following situations:1) Exact refraction index changes between microstructures;2)The gradient of average refractive index change between different tissue layers is parallel to the probe beam;3) The gradient of average refractive index change is vertical to the probe beam.The results show that the image distortion of situation 1) is usually negligible;in situation 2) there is a spread or shrink effect without relative location error; however,in situation 3) there is a significant image error inducing relative location displacement between different structures.Preliminary design to eliminate the distortion is presented,the method of which mainly based on the image classification and pixel array re arrangement.

The Flipping-Free Full-Parallax Tabletop Integral Imaging with Enhanced Viewing Angle Based on Space-Multiplexed Voxel Screen and Compound Lens Array

Tabletop integral imaging display with a more realistic and immersive experience has always been a hot spot in three-dimensional imaging technology,widely used in biomedical imaging and visualization to enhance medical diagnosis.However,the traditional structural characteristics of integral imaging display inevitably introduce the flipping effect outside the effective viewing angle.Here,a full-parallax tabletop integral imaging display without the flipping effect based on space-multiplexed voxel screen and compound lens array is demonstrated,and two holographic functional screens with different parameters are optically designed and fabricated.To eliminate the flipping effect in the reconstruction process,the space-multiplexed voxel screen consisting of a projector array and the holographic functional screen is presented to constrain light beams passing through the corresponding lens.To greatly promote imaging quality within the viewing area,the aspherical structure of the compound lens is optimized to balance the aberrations.It cooperates with the holographic functional screen to modulate the light field spatial distribution.Compared with the simulation results,the distortion rate of the imaging display is reduced to less than 9%from more than 30%.In the experiment,the floating high-quality reconstructed three-dimensional image without the flipping effect can be observed with the correct 3D perception at 96°×96°viewing angle,where 44,100 viewpoints are employed.

Distortion Identification Technique Based on Hilbert-Huang Transform in Video Stabilization

A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively.

A united optimum images fusion based on analysis of color distortion

In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and

Correction of Scanned Book Images

Because of the spine, a book cannot be set flat on a flat bed scanner. As a result, the image suffers from shape distortion, variable brightness, and blurred characters. This paper describes a method based on book shape analysis to address these problems. The algorithm utilizes the relation between the distance from a line segment on the book to the flat bed and its projected length. The correction process uses edge extraction, iterative boundary point computations, linear image transformation, and histogram modification. Experimental results show that the shape distortion and brightness variance problems are perfectly resolved by the method.

Distortion correction for the elemental images of integral imaging by introducing the directional diffuser

A distortion correction method for the elemental images of integral imaging（Ⅱ） by utilizing the directional diffuser is demonstrated. In the traditional Ⅱ, the distortion originating from lens aberration wraps elemental images and degrades the image quality severely. According to the theoretical analysis and experiments, it can be proved that the farther the three-dimensional image is displayed from the lens array, the more serious the distortion is. To analyze the process of eliminating lens distortion, one lens and its corresponding elemental image are separated from the traditional Ⅱ. By introducing the directional diffuser, the aperture stop of the separated optical system changes from the eye＇s pupil to the lens. In terms of contrast experiments, the distortion of the improved display system is corrected effectively. In the experiment, when the distance between the reconstructed image and lens array is equal to 120 mm, the largest lens distortion is decreased from 46.6% to 3.3%.

Design and optimization of resistive anode for a two-dimensional imaging GEM detector

A resistive anode for two-dimensional imaging detectors, which consists of a series of high resistivity pads surrounded by low resistivity strips, can provide good spatial resolution while reducing the number of electronics channels required. The optimization of this kind of anode has been studied by both numerical simulations and experimental tests. It is found that to obtain good detector performance, the resistance ratio of the pads to the strips should be larger than 5, the nonuniformity of the pad surface resistivity should be less than 20%, a smaller pad width leads to a smaller spatial resolution, and when the pad width is 6 mm, the spatial resolution （a） can reach about 105 μm. Based on the study results, a 2-D GEM detector prototype with optimized resistive anode is constructed and a good imaging performance is achieved.