Brief review of image denoising techniques

With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image quality.Therefore,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing noise.Each method has its own advantages and disadvantages.In this paper,we summarize some important research in the field of image denoising.First,we give the formulation of the image denoising problem,and then we present several image denoising techniques.In addition,we discuss the characteristics of these techniques.Finally,we provide several promising directions for future research.

Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation

Single image super-resolution is devoted to generating a high-resolution image from a low-resolution one,which has been a research hotspot for its significant applications. A novel method that is totally based on the single input image itself is proposed in this paper. Firstly, a local-feature based interpolation method where both edge pixel property and location information are taken into consideration is presented to obtain a better initialization. Then, a dynamic lightweight database of self-examples is built with the aid of our in-depth study on self-similarity, from which adaptive linear regressions are learned to directly map the low-resolution patch into its high-resolution version. Furthermore, a gradually upscaling strategy accompanied by iterative optimization is employed to enhance the consistency at each step.Even without any external information, extensive experimental comparisons with state-of-the-art methods on standard benchmarks demonstrate the competitive performance of the proposed scheme in both visual effect and objective evaluation.

Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient

Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance because texture containing obvious edges and large gradient changes is easy to be preserved as the main edges. In this paper, we propose a novel framework （DSHFG） for image smoothing combined with the constraint of sparse high frequency gradient for texture images. First, we decompose the image into two components： a smooth component （constant component） and a non-smooth （high frequency） component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art methods. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction, and image composition.

Quasi-Developable B-Spline Surface Design with Control Rulings

We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be as-developable-as-possible.Specifically,the terminal ruling lines are treated as hard constraints.Different from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control,our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed rulings.We treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in applications.The performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control(CNC)flank milling.

Image smoothing based on global sparsity decomposition and a variable parameter

Smoothing images,especially with rich texture,is an important problem in computer vision.Obtaining an ideal result is difficult due to complexity,irregularity,and anisotropicity of the texture.Besides,some properties are shared by the texture and the structure in an image.It is a hard compromise to retain structure and simultaneously remove texture.To create an ideal algorithm for image smoothing,we face three problems.For images with rich textures,the smoothing effect should be enhanced.We should overcome inconsistency of smoothing results in different parts of the image.It is necessary to create a method to evaluate the smoothing effect.We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems.A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter.Three evaluation measures:edge integrity rate,texture removal rate,and gradient value distribution are proposed to cope with the third problem.We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results.Experiments show that our algorithm is better than existing algorithms both visually and quantitatively.We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.