Guided filter-based multi-scale super-resolution reconstruction

The learning-based super-resolution reconstruction method inputs a low-resolution image into a network,and learns a non-linear mapping relationship between low-resolution and high-resolution through the network.In this study,the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images,and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task.The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image.To solve the problem of incomplete image features in low-resolution,this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering.The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image,and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network.The newly generated image effectively compensates for the details and texture information lost in the low-resolution image,thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme,the accuracy and speed of super-resolution reconstruction are improved.

Consistent image processing based on co-saliency

In a group of images,the recurrent foreground objects are considered as the key objects in the group of images.In co-saliency detection,these are described as common saliency objects.The aim is to be able to naturally guide the user's gaze to these common salient objects.By guiding the user's gaze,users can easily find these common saliency objects without interference from other information.Therefore,a method is proposed for reducing user visual attention based on co-saliency detection.Through the co-saliency detection algorithm and matting algorithm for image preprocessing,the exact position of non-common saliency objects(called Region of Interest here,i.e.ROI)in the image group can be obtained.In the attention retargeting algorithm,the internal features of the image to adjust the saliency of the ROI areas are considered.In the HSI colour space,the three components H,S,and I are adjusted separately.First,the hue distribution is constructed by the Dirac kernel function,and then the most similar hue distribution to the sur-rounding environment is selected as the best hue distribution of ROI areas.The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands.Experimental results show that this method effectively reduces the ROI areas'attraction to the user's visual attention.Moreover,comparing this method with other methods,the saliency adjustment effect achieved is much better,and the processed image is more natural.

Evaluating the robustness of image matting algorithm

In this study,the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm.This study evaluates consistent alpha masks based on the Gaussian-Hermite moment in combination with gradient amplitude and gradient direction.The gradient direction describes the appearance and shape of local objects in the image,and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image.They selected Gaussian blur,pretzel noise,and combined noise to destroy the image,and then evaluated the consistency of the original alpha mask and noise alpha mask.To determine the robustness of the matting algorithm,they assessed the degree of consistency of the alpha mask using three different evaluation levels.The experimental results show that noise has a greater impact on the performance of the matting algorithm,which shows a decreasing trend as the noise level in the image deepens.In noisy images,the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm.Different matting algorithms present different adaptations to different noises.

Filter-cluster attention based recursive network for low-light enhancement

The poor quality of images recorded in low-light environments affects their further applications.To improve the visibility of low-light images,we propose a recurrent network based on filter-cluster attention(FCA),the main body of which consists of three units:difference concern,gate recurrent,and iterative residual.The network performs multi-stage recursive learning on low-light images,and then extracts deeper feature information.To compute more accurate dependence,we design a novel FCA that focuses on the saliency of feature channels.FCA and self-attention are used to highlight the low-light regions and important channels of the feature.We also design a dense connection pyramid(DenCP)to extract the color features of the low-light inversion image,to compensate for the loss of the image's color information.Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

Computing knots by quadratic and cubic polynomial curves

A new method is presented to determine parameter values(knot)for data points for curve and surface generation.With four adjacent data points,a quadratic polynomial curve can be determined uniquely if the four points form a convex polygon.When the four data points do not form a convex polygon,a cubic polynomial curve with one degree of freedom is used to interpolate the four points,so that the interpolant has better shape,approximating the polygon formed by the four data points.The degree of freedom is determined by minimizing the cubic coefficient of the cubic polynomial curve.The advantages of the new method are,firstly,the knots computed have quadratic polynomial precision,i.e.,if the data points are sampled from a quadratic polynomial curve,and the knots are used to construct a quadratic polynomial,it reproduces the original quadratic curve.Secondly,the new method is affine invariant,which is significant,as most parameterization methods do not have this property.Thirdly,it computes knots using a local method.Experiments show that curves constructed using knots computed by the new method have better interpolation precision than for existing methods.

Saliency-based image correction for colorblind patients

Improper functioning, or lack, of human cone cells leads to vision defects, making it impossible for affected persons to distinguish certain colors. Colorblind persons have color perception, but their ability to capture color information differs from that of normal people: colorblind and normal people perceive the same image differently. It is necessary to devise solutions to help persons with color blindness understand images and distinguish different colors. Most research on this subject is aimed at adjusting insensitive colors,enabling colorblind persons to better capture color information, but ignores the attention paid by colorblind persons to the salient areas of images. The areas of the image seen as salient by normal people generally differ from those seen by the colorblind. To provide the same saliency for colorblind persons and normal people, we propose a saliency-based image correction algorithm for color blindness. Adjusted colors in the adjusted image are harmonious and realistic, and the method is practical. Our experimental results show that this method effectively improves images, enabling the colorblind to see the same salient areas as normal people.